Quantifying Resource Use in Computations

Quantifying Resource Use in Computations

R.J.J.H. van Son111 ACLC/University of Amsterdam, Spuistraat 210-212, 1012 VT Amsterdam, The Netherlands, R.J.J.H.vanSon@gmail.com. Licensed under the Creative Commons Attribution license
Abstract

It is currently not possible to quantify the resources needed to perform a computation. As a consequence, it is not possible to reliably evaluate the hardware resources needed for the application of algorithms or the running of programs. This is apparent in both computer science, for instance, in cryptanalysis, and in neuroscience, for instance, comparative neuro-anatomy. A System versus Environment game formalism is proposed based on Computability Logic that allows to define a computational work function that describes the theoretical and physical resources needed to perform any purely algorithmic computation. Within this formalism, the cost of a computation is defined as the sum of information storage over the steps of the computation. The size of the computational device, eg, the action table of a Universal Turing Machine, the number of transistors in silicon, or the number and complexity of synapses in a neural net, is explicitly included in the computational cost. The proposed cost function leads in a natural way to known computational trade-offs and can be used to estimate the computational capacity of real silicon hardware and neural nets. The theory is applied to a historical case of 56 bit DES key recovery, as an example of application to cryptanalysis. Furthermore, the relative computational capacities of human brain neurons and the C. elegans nervous system are estimated as an example of application to neural nets.
keywords: computation, compatibility logic, neural nets, cryptanalysis

1 Introduction

In June 1998, a high ranking USA official, Robert S. Litt, testified before a Senate judicial subcommittee that …decrypting one single message that had been encrypted with a 56-bit [Data Encryption Standard] key took 14,000 Pentium-level computers over four months; obviously these kinds of resources are not available to the FBI. Later the same year, a 56 bit DES key was recovered in 56 hours at a cost of less than $250,000 using 1536 custom chips [Ele98].

The DES example points to the lack of a computational work function as a fundamental problem in the theory of algorithms and computation. At the time, questions were raised about the security of 56 bit DES. In this debate, there was no way to estimate the resources needed to find a 56 bit key based on the available technology. So the above predictions could neither be supported nor defeated in a quantitative way, except by going to the expenses of actually cracking the keys.

A decade later, there still is no theoretical model for the abstract computational needs, or costs, of running an algorithm, nor a way to evaluate the computational capacity of customized hardware. This problem crops up more generally in game theory, eg, when defining costs in computational Nash equilibria [Hal08, HP08], and in computational complexity theory when modeling time and space bounded automata [DKV08, FH02]. In a practical sense, those who want to perform extensive computations have few tools to evaluate the computational power that current technology could (theoretically) provide.

At the other end of the spectrum of computational devices, neuro-informatics studies how neural networks and the brain compute [Gor03]. There is an acute interest in understanding how nervous systems compute behavioral responses to environmental challenges [Gor03, OHS09, Leh09, Eisnt]. Brain imaging and activity recording techniques, eg, fMRI, MER, and ERPs, can show subsets of neurons computing specific mental functions in real time. The local and long range connections between neurons can be mapped in detail [OHS09, Leh09, Eisnt]. The underlying questions are what is computed where, and how? One obvious intermediate question is what can actually be computed by a certain subset of neurons in a certain animal in a given time? This is again a question on resource use in computations, but now based on neurons instead of silicon gates.

In principle, it should be possible to compare the computational capacities of the nerve systems of different animals like it is possible to compare their metabolic rates. A human brain has on the order of neurons, whereas the nematode Caenorhabditis elegans has only 302 neurons in total (adult hermaphrodite, eg, [CP97]). But how can the computational work these different neurons perform be compared? This is a question that is currently impossible to formulate in a quantitatively meaningful manner.

The remainder of this paper is structured as follows. In section 2, a model is proposed for quantifying the resource use, or cost function, for performing a computation on theoretical and real devices (see also the Appendix). This model is applied to examples from cryptanalysis and neural physiology in section 3. The results are discussed in section 4.

2 A computational work function

Any universal computational work function should have a few general features. It should describe the resource needs of a computation in terms of costs. It should be abstract enough to be applicable to both theoretical and real devices. It must be able to add and remove resources during a computation. The cost must increase strictly monotonically and must be additive in serial and parallel computations. And finally, it must be possible to emulate any computational device efficiently, where “efficient” is formalized here as a linear cost dependency. An efficient emulator allows comparisons between different devices by comparing the sizes of emulator programs, independent of the emulated devices.

First, in section 2.1 a game model of computation will be formulated that identifies resources and deliminates what is part of the computation for which the costs must be calculated and what is not. Section 2.2 proposes a cost function which has the desired features. The proposed cost function defines a least-cost implementation for any computation for which an algorithm is known, which is explained in section 2.3. The cost function is then used to model the computational resources of silicon hardware (section 2.4) and neural networks (section 2.5).

2.1 The computability logic game model

Real computations need some material structure to carry and process the information, time and energy to allow state changes and to remove state information while increasing the entropy of the environment [LT07, Llo02, Llo05, Llo00]. So it is important to check whether the physics of computation does set limits on the resources needed in terms of the time, energy, and temperatures that are required and entropy that is generated. Of these factors, the minimum amount of energy to drive a bit sized state change in time is and the minimum dissipation needed to erase a bit is of the order , with Planck’s constant, the Boltzmann constant, and the absolute temperature. These values are important on a molecular scale, or in quantum computers, but not in current computers [Llo00]. So this study will ignore these physical constraints.

A theoretic framework that describes the qualitative use of resources in computations well is computability logic [Jap05, Jap06]. In computability logic, computability is defined in terms of games. The “computer”, or System, plays against the Environment and “wins” if it can complete the requested computation successfully using the available resources. This game model of computability explicitly defines what the responsibilities of the Environment are and how it interfaces with the System. It also accounts for the resources that are used by the System to perform the computation and how the system communicates the results. Therefore, it is very well suited to delimit and define the costs of computations.

Ignoring purely physical constraint, e.g., absolute time, temperature, and energy, in the cost function allows the use of a purely algorithmic game model from computability logic [Jap05, Jap06]. On this game model, a computational work function can be defined analogous to the cryptanalysis work function of Shannon [Sha49].

The current study will restrict itself to such a purely algorithmic and deterministic games where the speed of the moves is not relevant and the environment has unlimited capacities to execute moves [Jap05, Jap06]. In the framework of computability logic, the System doing the computation is further simplified by describing it as a collection of Universal Turing Machines [Tur36], UTMs, each with a Finite State Machine, FSM, doing the processing and three or more tapes: one or more work tapes, a valuation tape, and a run tape.

The work tape(s) correspond(s) to the working memory of a computer and contains the program and all related data in use. The run tape corresponds to an input/output medium that stores the moves written to it by the System and the Environment. The System can not move backwards on the run tape. That is, the System must use its own memory and cannot use the (free) run tape to store the in- and output history. The valuation tape contains the game specific parameters supplied by the Environment and used by the program. A more general interpretation of the valuation tape is that it contains any public information outside the control of the System.

The System can recruit as many computational devices, UTMs, as it wants by specifying them on the run tape. Every daughter device of the System can itself play against the Environment on its personal run tape and receives a personal valuation tape. Both the personal run and valuation tapes of each daughter device will be copies of the original System tapes. The communication between the UTMs that make up the System is modelled by simply letting their work tapes overlap, but other solutions are possible. Any UTM request should consist of a full description of the finite state machine, initial state, contents of the work tape, position of the heads, and the overlap between work tapes.

The computational model is completely interactive, so there are no general rules limiting what can be written to the run tape. To make the resource use explicit, it will be assumed that all moves are written as either fixed size or self delimited strings. Scanning the run tape for moves of the Environment is a computational cost that must be born by the System. To minimize that cost, the moments at which the Environment can write to a run tape are restricted. The Environment will only write to a run tape in response to a move of the device that “plays” on that run tape. Any daughter device of the System will go to sleep after it has written a move, and it wakes up only after the Environment has responded. The computational costs are defined on the work tape(s) and the processing units (UTMs), but not on the valuation and run tapes.

2.2 A simple cost function

A very simple work, or cost, function for a single UTM that has all the above features is

(1)

Where is the cost of a computation, is the number of steps needed to complete the computation, and is the information in bits, stored in that UTM at step (). In a situation with parallel UTMs, the cost is calculated for each UTM separately using the step cycles of that UTM. Shared memory is attributed to the UTM that makes the most steps.

The cost function in equation 1 replaces memory or time limited computations with a limitation in (c.f., [DKV08, FH02]). is an information measure that is linear in its components and always for any computation in progress. Therefore, in equation 1 is strictly monotonically increasing over “time” for any computation. The cost of a computation under equation 1 is linear in time and computational resources. So the cost of doing computations in parallel on different computational devices or in series on a single device is simply the sum of the costs of doing the individual computations in isolation (provided the Environment takes care of initialization of the System between computations). So equation 1 has indeed the compositional features requested above.

The information is the information needed to specify a UTM in the current state. That is, the information needed to specify at step the

  • action table

  • the current state

  • the position of the heads

  • the current contents of the working tape

The working tape of a UTM is potentially of infinite size. But at any moment of time, only a finite part of it is in actual use. For the cost calculations, it is assumed that only part of the working tape is actually “in use” and the contribution of each work tape cell is proportional to (where is the total number of possible symbols). Memory locations are considered “in use”, and part of the cost equation if they have been written to during initialization or during operation of the UTM.

This can be compared to the System “leasing” new stretches of tape as needed. It is here assumed new memory automatically enters equation 1 when an empty cell is written to. Some means for ending the “lease”, i.e., “freeing up” tape is allowed. This could simply be a special request on the run tape with an indicator of the working tape cells to be freed (eg, cells from the current head position). After such a request, the specified part of the work tape is not part of the cost equation anymore. The valuation and run tape are not factored in, as these are considered part of the Environment.

In a game context, the output moves of a UTM are only valid in a certain context where, in some sense, the output symbols get a meaning. To be able to compare the costs of a computation using different UTMs, they must all adhere to the same language on the output. A rigorous definition of the cost of running a program can most easily be given on a single computational device. An efficient emulater bridges the gap between different computational devices For every finite set of UTMs, it is straightforward to define a UTM that can efficiently emulate them all (see Appendix A).

If the cost of doing a computation on the original UTM in steps was , then the cost of doing that computation on the emulator, will be:

(2)

The constants and are specific for the emulator whereas is the “rounding error” of representing the original symbols and states in the symbols of the emulator. All three constants can be determined from the emulator program and structure. Examples of efficient emulators for UTMs and neural nets are given in Appendix A.

The cost function in equation 1 incorporates several trade-off relations. Most notably, a trade-off between processor complexity and length of computation in steps. A more complex computing device that processes more bits per step can reduce the cost of a computation if the memory use is large and vice versa. A specific case consists of a more complex device that can reduce the number of steps in a computation without increasing the amount of memory used. In such a case, the most efficient set up would be to select a processor with a size that is comparable to the average size of the memory used, (see Appendix B).

The cost function of equations 1 emphasizes a drawback of standard UTMs. No practical computer will enumerate all memory positions to access a specific memory site, as a standard UTM does, as this is not cost effective. Therefore, it will be assumed here that the UTM can extract a relative address from the action table that will let the head skip a number of cells on tape in a single clock cycle (i.e., processing step). This ability is related to the indirect addressing of register machines (e.g., Random Access Stored Program, RASP, or RAM machines).

Instead of adding a head skip with every entry in the action table, one or more accumulator/index registers could be added with some special states to manipulate them. However, the UTM with skip uses relative addressing, i.e., move head cells forward or backward, with a limited maximal skip. Furthermore, the relative position of the head over the tape is not explicitly stored (as a symbol) and is not accessible to the System. It might depend on the computation and UTM formulation whether the cost of the added complexity of the registers would be offset by the benefits.

The maximal number of cells that can be skipped in a single step affects the size of the action table, and the number of states and symbols, so this ability does not come for free. Going back from a UTM with symbols which allows for skipped cells to an equivalent UTM with only single cell moves, requires adding “move” states which remember the original state and read symbol, and move one step. The addition of these move states increases the total number of states needed by a factor and computation time by a factor . So the cost of a computation without skipped cells grows by a factor compared to a UTM with upto skipped cells (ignoring logarithmic terms).

To make the cost of performing a computation on a UTM complete, the cost of operating the read/write head of the UTM should be taken into account. The structure of the head follows directly from the action table. So the head of a UTM does not have to be specified separately. However, the head is the actual processing element and as such constructing and operating one adds costs to a computation. A model of the computational cost of a UTM head is presented in Appendix C. The head is specified by the action table, and, for larger systems, the cost of operating the head is generally smaller than the costs associated with the action table. Therefore, the contribution of the head to the cost of computations is ignored in the current study.

A quantitative example of complete cost calculations is presented in Appendix D for a minimal Tit-for-Tat game.

2.3 Least-cost implementation

A least-cost implementation can be defined in the same way as the algorithmic or Kolmogorov complexity [Cha69, LV97]. If a program is known that can perform a computation on a UTM in finite time, then the least-cost program can be found in a finite time too. The procedure is very simple and based on the fact that a program with a size larger than cannot run for a single step using less than resources. Run the original program and determine it’s cost . Now run all programs with sizes smaller than (a finite number of programs) and stop them if they have consumed in resources. All of these programs will stop executing either because they halt on their own, or because they overrun the cost limit. The program which needs the least resources to complete the computation is by definition the least-cost program.

To be able to compare different computational devices, eg, UTMs, all devices are required to generate their output in the same alphabet. This fits in the game formalism which requires the game participants to communicate in a shared language of “moves”. In the current context, a least-cost combination of can be defined within the set of UTMs that can be emulated by a specific emulator. The cost to be minimized is that of equation 2. In this setting, a program on tape and a “program” inside the processing unit become interchangeable.

The same procedure used between programs on a single UTM can now be repeated over all UTMs. Any UTM with a FSM size larger than cannot run even a single step within fixed cost bounds of . Determine the set of all UTMs with a size of their FSM . This is a finite set and can be emulated efficiently on a single device (see Appendix A). Run each of them with all programs with a size until they halt or have consumed in resources. Again, all these programs will stop. Select the pair which consumed the least resources as the least-cost option.

2.4 Relations with real hardware

The cost function of equation 1 is set in terms of stored information times number of steps the information is used. For non-storage hardware, this translates to the information put into the device, in terms of components and connections, and the operating frequency, ie, time per step. That is, the hardware of the computational device is treated as a “program”. The connections between the active elements, eg, transistors, are “programmable” to the degree they can be freely chosen during design.

Although it might be difficult to model a modern complex CPU in terms of component UTMs, it is possible to estimate the computational resources they generate by looking at the transistor counts. As the cost function only looks at memory “use”, the CPU complexity can be reduced to the information needed to describe the CPU state. That is, the variable state of the transistors and the fixed structure of the connections, ie, . It will be assumed, rather arbitrarily, that transistors are mainly connected locally (small world topology) and each transistor could on average have been connected in a hundred different ways ( bits). Also, a transistor has a 1 bit state, on or off, and the size of the “state machine” is ignored as it can be covered by the state+connections. Under these assumptions these numbers are and . Taken together, each transistor is guessed to contribute around bits to . This is, of course, just a very crude, ball-park estimate. It is straightforward to estimate the size of real computer systems from these principles.

As an example, the computational capacity of an off-the-shelf 2007 desktop system is estimated. An AMD 64 X2 CPU core is made up of around 50 million transistors, corresponding to byte of memory running at 3 GHz (2007, source Wikipedia). So the resources produced by two such cores on a CPU could be estimated at byte/second. 2 GB high speed dynamic RAM running at 400 MHz produces around byte/s. It is rather difficult to quantify magnetic disks, as it is not immediately clear what clock-speed would be most appropriate. A terabyte disk system would need a Hz clock speed to get in the same order of magnitude as the other subsystems, so it will be ignored for the moment. The on-chip caches are small in comparison ( byte/s) and will be ignored here too. All together, a modern system with dual-core CPU and 2 GB RAM will run at around byte/s, ie, at around 1 exabyte/s.

These data for general purpose CPUs can be compared to other types of devices. Recently, GPUs (Graphical Processing Units), originally designed to render graphics in personal computers and game consoles, are becoming popular in high performance computing [Str09, Val09]. A GPU can have half a billion transistors and runs at a half GHz with many parallel on-chip modules (data from 2007). For instance, the NVIDIA GeForce 8800 GT chip set contains 750 M transistors and runs at 0.6 GHz (source, Wikipedia, fall 2007). The crude metrics used here puts such a GPU at delivering byte/s without memory. This is close to half what a AMD 64 could deliver, but optimized for its task.

According to these measures, the original IBM PC with an Intel 8088 CPU (5 MHz, 29,000 transistors) and 0.1 MB memory would come in at about byte/s. Given the growth of computing power, decibels would seem to be a more convenient measure of resource size for a single computer in byte/second, eg, , using the scale of the original IBM PC as a reference. A dual-core AMD 64 system with 2GB RAM would then count as dB. Of course, equation 1 cannot be expected to reflect cost differences in real monetary terms. dB over 23 years (1984-2007) corresponds to an increase of roughly dB/year.

2.5 Relations with neurons

The same models as described above can in principle also be used to estimate the capacity of neurons in the brain. However, in neurons it is not yet clear what anatomical scale, and therefore, temporal scale, would be relevant to computation: the cell, the synapse, or even the neurotransmitter receptor. In addition, the current knowledge of neural computational functions and their relation to the neuro-physiology is fragmentary at best. Therefore, the estimates described below are only intended as illustrations of how the computational capacity of real neural nets might be modelled.

Assume the synapse is the relevant active element [yAFB03, RGnt] (“… a neuron is defined by synaptic connections” [RGnt]). Synapses are the contact points between neurons and it is generally believed that they mediate most of the computational and learning activity of the nervous system. The neuroanatomy of the human brain is far from settled [OHS09, Eisnt, Leh09] and it is difficult to put numbers on the populations of neurons and synapses with any precision. For this example, only general estimates will be used as can be found in textbooks. And the estimates will be limited to connections using chemical synapses. Each neuron receives input from up to synapses (eg, [MH07]). There are approximately neurons in the human brain (e.g., [Leh09]). So there are around synapses in a human brain. In general, a synapse will originate from a local, nearby, neuron. Take this local set to contain around neurons, which corresponds to connection distances of around 2 millimeters. The relative position of a synapse on the neural body is important for its function. For simplicity, the spatial structure of the neuron is reduced to the relative position of the synapses. Both the pre-synaptic and the post-synaptic part of the synapse can be in several (many) states describing it’s sensitivity to incoming action potentials and it’s ability to (de)polarize the post-synaptic membrane. As a last factor, the runtime delay of incoming action potentials will differ between different axon end points of the originating neuron. These differences have to be modeled too.

The above description treats the synapse as a static, passive, device and the estimates are in line with [DA06]. But biological neurons are dynamic, active, devices. This aspect of synaptic function is important to computations [PTK01]. This means that the computational capacity should include the complexity of the synaptic “device”. At the moment, it is completely unclear how the size in bytes of the complexity of the synapse should be estimated from physiological data.

3 Applications

3.1 Understanding the DES cracker example

The above theory might in future help support an informed discussion about the potential capabilities of modern computer hardware. That way, it might become less necessary to implement costly demonstrations just to show that a certain prediction is wrong, like the one presented by the FBI analysts. Looking at the DES cracker example from the Introduction, it is possible to estimate the computational resources available to the FBI and others at the time [Ele98].

The protagonists in the example used two well known approaches to estimate the costs of performing a computation. The public FBI approach was to take off-the-shelf systems, and estimate the run time and number of systems needed to perform the computation. The EFF approach was to design special purpose hardware and determine empirically what the requirements are in terms of number of systems and run time. The current study tries to base estimates on a combination of these approaches. This is done by trying to estimate what performance could be achieved if the most complex or powerful hardware available could be redesigned and optimized for the desired computation. That is, first estimate what, according to equation 1, the maximum computational costs are that can be handled by existing hardware in a given time on any computation (the maximal performance). Next estimate what the minimum cost is to perform the desired computation on optimized hardware. Then compare these two under the assumption that the existing hardware could be redesigned to be as good as the optimized hardware.

A 1998 Pentium II processor would have contained around transistors and ran at 400 MHz. This would account for approximately byte/s. A high end system in 1998 would have up to 256 Mbyte of 100 MHz main memory, which equates to byte/s. This brings the whole system up to around byte/s. 14,000 Pentium computers running for 4 months deliver byte (steps).

The Electronic Frontier Foundation, EFF, succeeded in designing a search unit in silicon that could check a 56 bit DES key in 16 clock cycles [Ele98]. The EFF were able to fit 24 such search units onto a single chip with around 10,000 transistors and use the units in parallel to check all possible keys. Many such chips can be used in parallel. Using the earlier ball-park estimate of a contribution to of 8 bit per transistor, the computational effort for a single encryption can therefore be estimated as byte (ignoring memory).

As one of the design goals of DES was easy implementation, this low figure should not be a surprise. If a general office computer of 1998 would have been a very efficient DES encryptor for its complexity, it would have been able to test keys a second (again, ignoring memory). A single such computer should find a key in less than 30 hours.

To evaluate the DES cracker, the housekeeping, communication and other functions are deliberately ignored. Attention is focused on the key search. The DES cracker chip could run with a clock speed of 40 Mhz. In total, 1536 chips were used each with around 0.5 Mbyte of memory. Together, this is or around byte/s for the chips and Mbyte memory on 40 MHz or only byte/s for the memory. Together, these specialized chips produce less as a computational resource than a single Pentium computer of the time, or less than the workstation used to coordinate the search. Running for 56 hours, the DES cracker chips delivered byte (steps). From this it can be concluded that the DES cracker set-up was seven orders of magnitude more efficient in DES encryption than a conventional computer of the day. Which is not really remarkable given the simplicity of the DES encryption algorithm.

Obviously, general office computers are all but efficient DES encryptors. Basically, the EFF used the fact that silicon is equivalent to a program: it is relatively easy to “program” a new chip to do exactly what is needed. If the FBI analysts [Ele98], or their critics, had been able to factor in the simplicity of the DES algorithm and the complexity of hardware of the time, they would have been better able to predict the vulnerability of the DES encryption.

3.2 Comparing human and C. elegans neurons

To describe each human brain synapse, an estimated 20 bits are needed to address the originating neuron out of a potential local population of 1 million. Some 10-13 bits might be needed to indicate the synapse’s relative position on the neural body. These 10-13 bits incorporate some of the spatial organization of the neural body. 8 bits each are allocated for the pre- and post-synaptic states, which might be a conservative estimate, given the complexity of synapses [RGnt]. The timing differences between synapses originating in the same neuron could be described in, eg, 4 bits. So a conservative estimate of the information needed to uniquely describe each synapse would be around 50 bit, or in the order of 6 byte. In total, on the order of byte (six petabyte) are needed to describe the state of all the synapses in a human brain. This is in accordance with the bit of [DA06] (Note that the estimate in [WLW03] is unphysical as it exceeds the Beckenstein bound for a brains sized object [Llo00])

The number of neurons is four orders of magnitude less than the number of synapses, so their contributions to the number of states are ignored. Action potentials have a maximum rate of approximately 500 Hz. So it would be prudent to estimate the step timing of synapses in the same range. That would mean that a conservative estimation of the human brain indicates that it calculates at a rate of byte/s.

The above estimations are based on a static synapse model. In reality, synapses are dynamic entities that adapt to stimulation [PTK01]. It is estimated here that two bytes are needed to describe the state of the synapse. To simplify matters, it is assumed that 10 bits of these are needed to describe dynamic state parameters. To get at least an order of magnitude estimate, the action table size of a UTM with the same number of states, , is used as a proxy measure. From this it follows that the complexity of the synapse is of the order of bytes (on the order of 10 bits per state). This increases the estimated capacity of the human brain to something in the order of byte/s.

Compare the human central nervous system to the neural system of C. elegans [CP97]. An adult hermaphrodite contains 302 neurons and around 7000 synapses. Each neuron has on average around 25 incoming synapses. That is, the originating neuron can be described in 8 bit and the position of the incoming synapse on the neural body in around 4 bits. Timing differences in incoming synapses can probably be ignored (0 bit). It is unclear how the pre- and postsynaptic state information relates between nematodes and mammals, but here it is arbitrarily assumed that nematodes will need less bits, just to put a number on it, 10 instead of 16 bits. In total, around 22 bit would be needed to completely describe the state and position in each synapse in a nematode, or less than 3 bytes. This is assuming only static synapses. Again, the contribution of the neurons is partly included in the post-synaptic state, and partly ignored.

As nematodes are not homeiotherm, the switching speed of the synapses will be lower than in mammals. For a ten degrees difference in body temperature ( versus C), at least a halving of the metabolic rate, and switching speed, is expected. Using only the values for static synapses, the nervous system of a complete hermaphrodite adult C. elegans would then have a computational capacity of byte per second. A single human neuron would have synapses each needing around 6 bytes to describe statically, working at Hz for a total of byte per second. So, according to these crude, ballpark, estimations, a single human brain neuron processes, or computes, around six times as much information than the complete neural system of a C. elegans adult.

It is informative to look at what makes individual human neutrons perform at a higher level than the complete neural system of C. elegance. The important factors are 1) number of synapses, 2) population of possible originating neurons, 3) spatial interactions between synapses on a neuron, 4) metabolic speed.

1) The number of synapses ending on a single human neuron and in the complete C. elegance body are comparable (7,000 versus 10,000). As it is assumed that the synapses are the computational entities, this fact alone predicts comparable performance.

2) Each synapse in C. elegance can originate in some 300 other neurons. This corresponds to some 8 bit to describe the possible information processing wirings. Each human synapse can originate, potentially, from other neurons. Here it is assumed that connections in the human brain are in general local (a small world network) and the real, or effective, number of originating neurons in the human brain is much more limited. But any realistic number for the human brain will be way larger than the 300 in C. elegance. In our example this is simply limited to a million originating neurons, i.e., 20 bits. But even with only a 20,000 possible originating neurons this would still be double the contribution of a C. elegance synapse.

3) With synapses contacting each human neuron compared to the 25 synapses contacting each C. elegance neuron, the options for spatial interactions between synapses increases. In our simple model this increases the computational power of human neurons from approximately 4 to 13 bits.

4) Last, there is an expected metabolic speed doubling, from 25C to 37C, which would double computational performance.

4 Discussion and conclusions

Almost from the start of the computer era, questions about the time and memory needed to complete a computation were raised [FH02]. A lot of theoretical progress has been made towards these questions in the fields of game theory, logic, and computational complexity. The current study tries to bring these developments a step closer to the practical developments in other fields, eg, cryptanalysis, neuro-imaging, and neuro-informatics. A pressing need in these latter fields is an evaluation of the computational resources of an actual processor, eg, the electronic hardware or neuronal wet-ware, and to link these to the theoretical powers of Turing Complete theoretical devices, eg, the UTM. This inclusion of the processing hardware in the accounting of the resources is a challenge which requires a way to valuate memory and processing hardware in a uniform currency that can be integrated with, or in, time.

Based on a few natural requirements, a simple formula for a computational work function for quantified resource use emerges with the features of Memory times Steps, ie, a dimension of bytes (equation 1). In more intuitive physical terms, the computational resources are counted as an integration of information (entropy) over a normalized interaction time. This count includes the information frozen into the computational device itself, eg, the UTM action table, the silicon of the CPU, or the neurons and synapses in a nervous system.

This definition of the cost of a computation directly leads to the concept of a least-cost implementation, both for a single computational device and between devices. Such a least-cost implementation can always be found within a finite time given a single example program that can perform the computation. As such, the least-cost is a universal invariant of the computation.

In the end, computing is done using some physical substrate. This substrate, eg, silicon chips or neural tissue, will need to have some, non-random, structure to be able to run a program, eg, transistors, synapses, and most of all connections. The information stored in this structure, as far as it is relevant to computations, is the needed to calculate the computational costs of equation 1.

Reducing silicon CPU complexity to concrete hardware design features like transistor count and connectivity, and memory capacity, it is possible to roughly guess both the capacity of real computer hardware and the hardware needs of (simple) algorithms. A more refined model, that takes into account what level of complexity can be achieved by custom hardware, can be used to estimate the real costs of implementing and executing abstract mathematical algorithms. This would allow, for instance, security analysts to be better prepared to the increasing power of computer hardware than they currently are.

The same models can also be used to estimate the capacity of neurons in the human or animal nervous system. These estimations are currently rather speculative. But it can be easily shown on elementary neuro-anatomical and neuro-physiological arguments that each individual human brain neuron should outperform the complete nervous system of C. elegans by almost an order of magnitude.

It is even possible to compare the computational capacity of neural nets and silicon. However, this does not lead to a lot of insight immediately. Neurons and silicon are on different ends of the computational spectrum. The computational capacity of silicon is dominated by it’s clock speed. On the other hand, neurons are slow, but the capacity of neural nets is dominated by their connectivity. This formalizes the well known fact that the computational strengths of human brains and silicon computers lie in completely different problem areas. Simulating the one in the other has always proved to be extremely inefficient.

5 Funding

Netherlands Organization for Scientific Research (276-75-002)

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Appendix A Efficient emulators

The Efficient Emulator requirement can be defined as follows: Given a finite set of computational devices of a certain complexity, eg, UTMs with up to tapes and action table sizes of unto bits, that can perform computation in steps with cost , there exists a computing device which can emulate any of these devices performing at a cost such that (by definition)

(A-1)

For a UTM emulator, can be interpreted as and as . For a UTM, , , and are fixed emulator cost factors for all emulated devices and computations and represents the “rounding error” in representing the original states and symbols on the emulated device, eg, UTM, in the symbols of the emulator, eg, UTM. The size of the rounding error can be estimated from the encoding of the emulated device, eg, the action table.

Efficient emulation according to equation A-1 is possible using equation 1 as a cost function at least for some Turing Complete devices. Which means that any algorithm that can be computed efficiently by one device, eg, a UTM, can also be computed efficiently by other devices. It is a weak condition as it does not ensure that there will always be an efficient emulator of a specific type.

If the lowest cost, , of a certain computation on any efficient emulator is known, it can be shown that the cheapest program on any emulated device, eg, UTM, that can perform the same computation in steps has a cost of at least

(A-2)

Where itself depends on (see equation 1).

Below, two examples of efficient emulators are given. One emulates all single tape UTMs up to a given number of symbols and states. The other emulates simplified neural nets unto a maximal number of nodes and connections.

a.1 An efficient emulator of UTMs

For any UTM with tapes, it is possible to design a tape UTM that can emulate it efficiently. Here this is proven for , but other cases and types of devices follow directly from this case. The dual tape UTM uses one tape, with head , to store the tape of UTM. The other tape, with head , contains the action table of UTM, organized as a table of
{New symbol , Move , Move } addressed by row addresses {State UTM, Symbol on }. The action table of UTM has a simple structure, and will not be described here.

At the start of an emulated read-write-move cycle of UTM, the position of the head, , over the tape indicates the current state of UTM. UTM performs the following steps:

  1. Move to correct row on in stored action table of UTM

    • Read current symbol from tape

    • Move by rows

  2. Read and write new symbol

    • Read new symbol from

    • Write to tape

    • Move to next field in row

  3. Move

    • Read movement from

    • Move by

    • Move to next field in row

  4. Move to new state of UTM

    • Read movement from

    • Move by to the row position that indicates the new state of UTM

The state of UTM will move UTM to a program tape area that will halt UTM.

It is obvious that UTM can only efficiently emulate single tape UTMs for which it can handle all symbols, states, and head movements inside it’s own tape symbols. This sets an upper size limit to the UTMs it can emulate. But within these size limits, this emulator clearly works according to equation A-1. UTM can emulate every single read-write-move cycle of any UTM in four of its own read-write-move cycles (). The action table of UTM can be stored in rows of 3 symbols of UTM, which takes more space than bits by . The action table, state and other work tape contents of UTM are fixed, contributing per emulated read-write-move cycle of UTM for a total of (). Starting and halting costs are also of order .

a.2 An efficient emulator for neural nets

An efficient emulator for a simplified neural network can be build from parallel processors. Such an emulator will be generated as above for a UTM. In a general game model, a UTM is recruited for each neural node and then a UTM for each synapse or connection between neural nodes. There are a maximum of nodes available each with at most incoming connections (synapses). In total, a maximum of synapses will be available. Unused nodes and synapses are unconnected and have empty worktapes, but they do contribute to the computational cost of the emulator. All UTMs have dual work tapes, stores the node and synapse states and a state transformation table. The work tapes of the synapse UTMs will overlap with a shared state field in the work tape of the originating neural node UTM and a ”personal” activity field in the target neural node UTM. It is assumed that an unlimited number of UTMs can read concurrently from the same field of a shared work tape. However, only one UTM at a time can write to a shared field.

Each synapse UTM has a table that tells how a current synapse state changes to a new state under influence of the state, , of the originating node ( or , for firing a spike or not). The table contains a row for every possible synapse state. Each row contains fields:
{Activation, Head movement for , Head movement for }
The relation between the synapse state (row number) and the Activation is the weight of the synapse. “Learning” could result in changing the activation entries (not implemented here).

Start at the work tape position of the head over that contains the state of the originating node and head over the start of the table row on that contains the current state of the synapse. Then first update all the synapse UTMs in parallel.

  1. Synapse UTMs read the state of the originating node UTMs

    • Read node state , which is either or (fire spike)

    • Move to next field containing the current activation

    • Move to the row field corresponding to the node state

  2. Read new synapse states from

    • Read from as a relative head movement

    • Move by

  3. Read and write activation of corresponding synapse states

    • Read Activation from

    • Write Activation to

    • Move to previous field

Then update the neural node UTMs. The head, starts at the first Activation field (of fields) on . contains a table to relate the new state to the activation level. The table is organized in rows with {New State, New Activation}. The new activation level which follows the current, is stored as a movement of . The position of indicates the current activation of the node.

  1. Sum activation fields ( steps), end over node state field

    • Read activation from

    • Move by rows

    • Move to next field

  2. Read new state and update node state

    • Read new state from

    • Write new state to

    • Move to next field on

  3. Update node activation state

    • Read new activation state from

    • Move back to first activation field (ie, by fields)

    • Move to new activations state
      (eg, start of the current row for state , and to the start row of the table after state , spike generation)

With the exception of step 4, all steps take a single cycle of the UTMs. In total, a single cycle on the original neuron can be emulated in steps of all the UTMs in parallel.

Step 4 is extremely inefficient because it needs steps to sum the activations, and every neural network solves the problem by using a fast integrator. Such an integrator will sum the synapse activations in a short time. This integration can be done by a fast or parallel accumulator.

Assume that all activation symbols are two’s complement bit numbers (to allow for inhibitting synapses) that indicate the size of the activation. The accumulator would contain a register with bits representing the current activation and an adder with full bit adders. A one bit full adder has a truth table of bits, 3 bits for inputs and carry-in to indicate the row and 2 bits for output and carry-out. The first and last bit adders need only half as much, 8 bits, because they lack a carry-in or carry-out.

If the truth tables are used as the complexity of the adders, the bit accumulator would need bit registers (accumulator, input, and carry bits) and bit truth tables, or, bits. In this calculation the two half bit adders are combined.

So for a 32 bit activation size, the accumulator would need around 591 bits. A parallel integrator can be simulated by a fast accumulator which sums the activation fields in a single clock step. That is, step 4 is performed in a single step by the accumulator which sums all the activations and prints out a selection of bits from the accumulator (not necessarily all bits). The cost of such a fast accumulator would be per clock step. Note that this is approximately the same cost as would be needed for parallel accumulators working in a single step. Then step 5 is changed to read the activation and generate the spike (1) or not (0).

The original cost, , of a computation of a neural network with nodes and synapses per node, from originating nodes, over steps is

(A-3)

Where and are the total information size of the neuron nodes and synapses, respectively. Here, the complexities are estimated as the sizes of the action tables of equivalent UTMs, as there is currently no sensible estimate based on physiological data. With a fast accumulator, the simulation of a node splits the complexity into an accumulator part () and “the rest” (), ie, .

To calculate the cost of the emulation, using the fast accumulator, the sizes of the emulator UTMs and accumulator without tapes are , , and for emulating the node body, accumulator, and synapse, respectively. The corresponding rounding errors for emulating the real neural states and symbols in the emulator are , , and . For nodes with each synapses and bits to designate the originating node, the emulator cost becomes:

The cost in equation A.2 is indeed linear in , and according to equation A-1, for fixed maximum and .

Appendix B Complexity versus time trade-off

Using a more complex Finite-State-Machine (FSM) often reduces the time and cost needed to complete a lengthy computation. On the other hand, moving a short computation to a smaller device can reduce costs too. The boundaries of such trade-offs follow from the cost function. As an example, consider a UTM with states, symbols and possible head movements. UTM has a FSM size , where are the bit sizes needed to store, respectively, states, symbols, and head movements. Assume there is an efficient, low-cost, program for UTM that computes in steps effectively using bits on tape (where ) with cost . At each step, UTM can process bits. Assume that depends on the total number of bits processed, .

Construct a new UTM that can process bits per step, or

(B-1)

and take . Very simple examples of such operations would be to combine program steps for parallel execution to increase , or to split program steps into smaller components to decrease .

The new UTM is chosen such as to reduce the cost of computation . UTM has a FSM size, , of

(B-2)

Simplify the new FSM size to where can be roughly approximated as an exponential function of , . A new, efficient, program on UTM can calculate by processing bits or in steps. As the total number of bits processed remain the same, it is assumed that .

The cost, , of computing using on UTM can be estimated as . After ignoring small components and , the new cost becomes

(B-3)

For large , the new cost becomes which is a decrease if . For small , the new cost becomes which is a decrease if . Note that in the limits of and the costs can be made very small indeed by, respectively, increasing or decreasing .

The optimal size of the FSM can be estimated by calculating the minimum of equation B-3. Express the effective memory size in terms of the FSM size, and assume that . Differentiate with respect to . The minimum cost is reached if:

(B-4)

The optimal size of a FSM is reached if , which means that the minimal cost is reached if .

The above boundaries on the cost are for the ideal cases, where both the memory use, , as the number of steps, , are minimal. In general, a cost reduction to can be found for large, , if

(B-5)

These results suggest that the optimum results are found for choices of for which . This implies that for .

The above modelling refers to computations that are processor bound, ie, the computations depend on the number of bits processed. For such a computation, the most efficient implementation should try to reduce the number of computational steps by equalizing the complexity (“size”) of the central processor and the amount of memory used.

Appendix C The cost of operating the UTM head

In a UTM, the head is the “processing element”. The head reads and writes symbols, and steps forward and backward. It can also be seen as responsible for changing the state of a UTM. The structure of the head is fully determined by the actions table, ie, number of states, symbols, and possible head movements. So it does not have to be specified in the definition of a UTM. However, the complexity of the moving head adds to the real costs of operating a UTM.

The complexity of the UTM head can be estimated, in symbolic terms, from the number of symbols and head movements. For symbols, at least bits are needed for each of the read and the write functions. Head movements over the tape and state changes in the action table will be implemented as counters that keep track of the relative movements over the tape and the action table and signals when zero is reached (count down).

For each bit in a counter, 2 bits are needed for the register and carry-in, 2 bits for the output and carry-out and bits for the truth table. In a counter, only the carry-in bits are counted as the carry-out bits are the same bits. In total, 11 bits are needed per counter bit. The last counter bit does not need a carry-out bit and only needs a 4 bit truth table. So a counter of width , needs bit of ”content” for the bare counter. A compare-to-zero can be implemented as a logical OR over the bits of the counter that is triggered by the result 0/false. This can be implemented by an OR of each bit with the result of the higher order bits. For each bit, except the highest order bit, two inputs and one output and a 4 bit (OR) truth table are needed, where all but the last output are shared with the next input. Together, 6 bits per counter bit for a total of bits for a counter of width . So a counter plus zero comparator with bits needs bits of logic storage. Note that the information needed to describe the connections is ignored here for simplicity.

With states, the state counter into the rows of the action table needs a width of bits and a total content of bit. To address the columns in the action table with symbols, the counter width is with a total content of bit. For a maximal range of steps, the tape counter will need bits width and a total content of bits. With one bit dedicated to the direction of movement, the latter might be reduced by 18 bit. For the purpose of generality, the full bits will be used here. Operating a state or tape counter running , , and steps would cost, respectively, , , and bit (steps) of our work function.

In total, bits are needed to specify the state of the head during operation for a cost of:

(C-1)

Where is the cost of running the head in bits and is the length of the computation in clock steps. Equation C-1 only represents the minimum cost in symbolic (bit) terms.

Reducing the UTM head to one that does not skip tape cells, (i.e., ), increases the number of steps needed to complete the computation by a factor of and increases the number of states needed, and the size of the action table, by a factor of to store state and symbol information while stepping to the desired tape cell. So the cost of running the computation increases by a factor of , both when accounting for the action table size and when accounting for the state counter cost (ignoring logarithmic terms). The tape counter will run at approximate the same cost as the decrease in the number of counts compensates for the increased duration of the computation, ignoring logarithmic factors. The cost of the symbol read and write heads and the symbol counter will increase by a factor due to the longer compute times.

The cost of using a UTM can be divided into the size of the tape and action table, and the cost of deploying the head. For a fully 8 bit UTM, , the head will account for just over 6% of the non-tape cost (107 kb versus 1.57 Mb for the action table), down to under 0.03% for a fully 16 bit UTM ().

From the definitions it can be derived that, for large and , the cost of running the head becomes small compared to the cost of the action table if . This is satisfied if and . Both conditions are not unreasonable for practical systems doing long computations. See Appendix B for trade-offs between , , , and .

The information to prescribe the UTM head can be extracted from the action table and does not have to be specified independently. Moreover, for UTMs which are not minimalist, the contribution of the head to the costs of the computation will be relatively small. To simplify this study, the contributions of the head to the costs of computations will, therefore, be ignored in this paper.

Appendix D A quantitative cost example: Tit-for-Tat

To illustrate the cost computations in the game model, it will be applied to the Iterated Prisoner’s Dilemma game with Tit-for-Tat as the strategy [AH81]. The game is played on a single Run tape, where the moves of the System and Environment are written in pairs of cells.

There are three symbols: for cooperate, for defect and for halt. The environment starts a turn by writing a string of two cells, one with a random symbol or , and one with the Environment’s move, either or . Then the Environment wakes up the System which is always positioned on the first cell of the string where it is supposed to write a move. The System completes the turn in a two step cycle. Note that the System has no private tape and cannot move backward over the Run tape.

First, the system reads the content of the cell it is positioned over and writes its current move, or , into the cell. If the symbol read was , the System halts and the game is over, else the System moves the head to the next cell. In the next step, the System reads the symbol in the underlying cell, moves to the next (empty) cell and goes to sleep (if that cell is empty). Then the Environment generates the next turn.

The Tit-for-Tat strategy is implemented in a simplified Turing Machine with five states: Cooperate (), Defect (), Read (), and Halt (). There are three symbols, (cooperate), (defect), and (halt). The System cycles through the turns as follows:

  1. cycle

    • Wake up by Environment

    • Read content of Run tape

    • Depending on the current state write:

      • if state is

      • if state is

    • Move to next cell

    • If read symbol was:

      • switch to

      • switch to

      • switch to

  2. cycle

    • Read content of Run tape

    • Do not write (or write back the read symbol)

    • Move to next cell

    • If read symbol was:

      • switch to

      • switch to

      • switch to

The game starts by writing the specification of the Tit-for-Tat program on the valuation tape with as the initial state. The Environment loads the program, writes its first move and positions the System over the first cell on the Run tape in state , and starts the System. The game ends when the Environment writes an symbol which halts the System.

The System goes to “sleep” when it reaches an uninitialized tape cell. If the Environment writes all its moves in one go, the System will not go to sleep and play until it reaches an symbol. Else it will sleep until the cell under its head is initialized.

The action table of the Tit-for-Tat player is presented in table 1.

Symbol read
State H
c 1 1 0
d 1 1 0
r 1 1 C 0
symbol write - move - new state
Table 1: Action table for Tit-for-Tat player. The System is not actually allowed to overwrite the move of the Environment. For completeness, the system is set to rewrite the Environment’s move in state .

This simplified implementation is very small, 4 states and 3 symbols. After entering the state the System halts and the game is over. The System cannot write the symbol. There is a rule that the Environment is not allowed to write an symbol in its second, move, cell because it would lead to an incomplete game. If the Environment does make this illegal move, it loses. The fact that the System writes a symbol in the same cell afterward (lower right hand side cell of table 1), effectively breaking the rule that is not allowed to change the move of the Environment, does not change this outcome. In compatibility logic, the player who makes the first illegal move loses.

The total action table could fit in 36 bit ( bits per row and 3 rows) and the current state in 2 bits. One turn would take two clock cycles. The cost for the System of running the game would be 76 bits per turn () as sleep time is not counted. It is easy to see how the complexity of the System’s game playing strategy can be increased by including one or more private work tapes and more states. However, such a more complex strategy would increase the costs of the computation, potentially by a very large amount.

If the cost of running the head is included, with symbols, states, and movement options, the complexity of the run tape head would be bit for writing (2 symbols) and bit for reading (3 symbols), bit for the head movement counter, and bit each for the state change and symbol counter. The cost of running the head would be bit per move (per step: bit). So running the head would be the major cost of running this Tit-for-Tat Machine.

Some aspects of computability logic have been used implicitly in this example. Most notably the fact that any player who breaks the rules loses. So if any of the players would rewrite any of the moves, it would lose. The Environment can read any cell, and therefore, has to write its moves first in every turn or else it could cheat. The System cannot move back over the Run tape, so it has to write down its own move before it can read the Environment’s move or else forgo this turn. It is a free design choice to go for a game structure that prevents this type of cheating instead of a rule to bind the players. Both approaches would work. It is the Environment who determines whether the System has completed the computation and, therefor, “wins”. This means the System is not required to keep track of the score, which would be costly. In this Tit-for-Tat game, the condition for winning could be anything from not breaking the rules to actually getting the most points.

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