Building fast Bayesian computing machines out of intentionally stochastic, digital parts.
The brain interprets ambiguous sensory information faster and more reliably than modern computers, using neurons that are slower and less reliable than logic gates. But Bayesian inference, which underpins many computational models of perception and cognition, appears computationally challenging even given modern transistor speeds and energy budgets. The computational principles and structures needed to narrow this gap are unknown. Here we show how to build fast Bayesian computing machines using intentionally stochastic, digital parts, narrowing this efficiency gap by multiple orders of magnitude. We find that by connecting stochastic digital components according to simple mathematical rules, one can build massively parallel, low precision circuits that solve Bayesian inference problems and are compatible with the Poisson firing statistics of cortical neurons. We evaluate circuits for depth and motion perception, perceptual learning and causal reasoning, each performing inference over 10,000+ latent variables in real time — a 1,000x speed advantage over commodity microprocessors. These results suggest a new role for randomness in the engineering and reverse-engineering of intelligent computation.
The authors contributed equally to this work.
Computer Science & Artificial Intelligence Laboratory, MIT
Department of Brain & Cognitive Sciences, MIT
Our ability to see, think and act all depend on our mind’s ability to process uncertain information and identify probable explanations for inherently ambiguous data. Many computational models of the perception of motion, motor learning, higher-level cognition[3, 4] and cognitive development are based on Bayesian inference in rich, flexible probabilistic models of the world. Machine intelligence systems, including Watson, autonomous vehicles and other robots and the Kinect system for gestural control of video games, also all depend on probabilistic inference to resolve ambiguities in their sensory input. But brains solve these problems with greater speed than modern computers, using information processing units that are orders of magnitude slower and less reliable than the switching elements in the earliest electronic computers. The original UNIVAC I ran at 2.25 MHz, and RAM from twenty years ago had one bit error per 256 MB per month. In contrast, the fastest neurons in human brains operate at less than 1 kHz, and synaptic transmission can completely fail up to 50% of the time.
This efficiency gap presents a fundamental challenge for computer science. How is it possible to solve problems of probabilistic inference with an efficiency that begins to approach that of the brain? Here we introduce intentionally stochastic but still digital circuit elements, along with composition laws and design rules, that together narrow the efficiency gap by multiple orders of magnitude.
Our approach both builds on and departs from the principles behind digital logic. Like traditional digital gates, stochastic digital gates consume and produce discrete symbols, which can be represented via binary numbers. Also like digital logic gates, our circuit elements can be composed and abstracted via simple mathematical rules, yielding larger computational units that whose behavior can be analyzed in terms of their constituents. We describe primitives and design rules for both stateless and synchronously clocked circuits. But unlike digital gates and circuits, our gates and circuits are intentionally stochastic: each output is a sample from a probability distribution conditioned on the inputs, and (except in degenerate cases) simulating a circuit twice will produce different results. The numerical probability distributions themselves are implicit, though they can be estimated via the circuits’ long-run time-averaged behavior. And also unlike digital gates and circuits, Bayesian reasoning arises naturally via the dynamics of our synchronously clocked circuits, simply by fixing the values of the circuit elements representing the data.
We have built prototype circuits that solve problems of depth and motion perception and perceptual learning, plus a compiler that can automatically generate circuits for solving causal reasoning problems given a description of the underlying causal model. Each of these systems illustrates the use of stochastic digital circuits to accelerate Bayesian inference an important class of probabilistic models, including Markov Random Fields, nonparametric Bayesian mixture models, and Bayesian networks. Our prototypes show that this combination of simple choices at the hardware level — a discrete, digital representation for information, coupled with intentionally stochastic rather than ideally deterministic elements — has far reaching architectural consequences. For example, software implementations of approximate Bayesian reasoning typically rely on high-precision arithmetic and serial computation. We show that our synchronous stochastic circuits can be implemented at very low bit precision, incurring only a negligible decrease in accuracy. This low precision enables us to make fast, small, power-efficient circuits at the core of our designs. We also show that these reductions in computing unit size are sufficient to let us exploit the massive parallelism that has always been inherent in complex probabilistic models at a granularity that has been previously impossible to exploit. The resulting high computation density drives the performance gains we see from stochastic digital circuits, narrowing the efficiency gap with neural computation by multiple orders of magnitude.
Our approach is fundamentally different from existing approaches for reliable computation with unreliable components[13, 14, 15], which view randomness as either a source of error whose impact needs to be mitigated or as a mechanism for approximating arithmetic calculations. Our combinational circuits are intentionally stochastic, and we depend on them to produce exact samples from the probability distributions they represent. Our approach is also different from and complementary to classic analog and modern mixed-signal neuromorphic computing approaches: stochastic digital primitives and architectures could potentially be implemented using neuromorphic techniques, providing a means of applying these designs to problems of Bayesian inference.
In theory, stochastic digital circuits could be used to solve any computable Bayesian inference problem with a computable likelihood by implementing a Markov chain for inference in a Turing-complete probabilistic programming language[19, 20]. Stochastic ciruits can thus implement inference and learning techniques for diverse intelligent computing architectures, including both probabilistic models defined over structured, symbolic representations as well as sparse, distributed, connectionist representations. In contrast, hardware accelerators for belief propagation algorithms[22, 23, 24] can only answer queries about marginal probabilities or most probable configurations, only apply to finite graphical models with discrete or binary nodes, and cannot be used to learn model parameters from data. For example, the formulation of perceptual learning we present here is based on inference in a nonparametric Bayesian model to which belief propagation does not apply. Additionally, because stochastic digital circuits produce samples rather than probabilities, their results capture the complex dependencies between variables in multi-modal probability distributions, and can also be used to solve otherwise intractable problems in decision theory by estimating expected utilities.
Stochastic Digital Gates and Stateless Stochastic Circuits
(Figure 1 about here)
Digital logic circuits are based on a gate abstraction defined by Boolean functions: deterministic mappings from input bit values to output bit values. For elementary gates, such as the AND gate, these are given by truth tables; see Figure 1A. Their power and flexibility comes in part from the composition laws that they support, shown in Figure 1B. The output from one gate can be connected to the input of another, yielding a circuit that samples from the composition of the Boolean functions represented by each gate. The compound circuit can also be treated as a new primitive, abstracting away its internal structure. These simple laws have proved surprisingly powerful: they enable complex circuits to be built up out of reusable pieces.
Stochastic digital gates (see Figure 1C) are similar to Boolean gates, but consume a source of random bits to generate samples from conditional probability distributions. Stochastic gates are specified by conditional probability tables; these give the probability that a given output will result from a given input. Digital logic corresponds to the degenerate case where all the probabilities are 0 or 1; see Figure 1D for the conditional probability table for an AND gate. Many stochastic gates with m input bits and n output bits are possible. Figure 1E shows one central example, the THETA gate, which generates draws from a biased coin whose bias is specified on the input. Supplementary material outlining serial and parallel implementations is available at . Crucially, stochastic gates support generalizations of the composition laws from digital logic, shown in Figure 1F. The output of one stochastic gate can be fed as the input to another, yielding samples from the joint probability distribution over the random variables simulated by each gate. The compound circuit can also be treated as a new primitive that generates samples from the marginal distribution of the final output given the first input. As with digital gates, an enormous variety of circuits can be constructed using these simple rules.
Fast Bayesian Inference via Massively Parallel Stochastic Transition Circuits
Most digital systems are based on deterministic finite state machines; the template for these machines is shown in Figure 2A. A stateless digital circuit encodes the transition function that calculates the next state from the previous state, and the clocking machinery (not shown) iterates the transition function repeatedly. This abstraction has proved enormously fruitful; the first microprocessors had roughly distinct states. In Figure 2B, we show the stochastic analogue of this synchronous state machine: a stochastic transition circuit.
Instead of the combinational logic circuit implementing a deterministic transition function, it contains a combinational stochastic circuit implementing a stochastic transition operator that samples the next state from a probability distribution that depends on the current state. It thus corresponds to a Markov chain in hardware. To be a valid transition circuit, this transition operator must have a unique stationary distribution to which it ergodically converges. A number of recipes for suitable transition operators can be constructed, such as Metropolis sampling  and Gibbs sampling; most of the results we present rely on variations on Gibbs sampling. More details on efficient implementations of stochastic transition circuits for Gibbs sampling and Metropolis-Hastings can be found elsewhere . Note that if the input represents observed data and the state represents a hypothesis, then the transition circuit implements Bayesian inference.
We can scale up to challenging problems by exploiting the composition laws that stochastic transition circuits support. Consider a probability distribution defined over three variables . We can construct a transition circuit that samples from the overall state by composing transition circuits for updating , and ; this assembly is shown in Figure 2C. As long as the underlying probability model does not have any zero-probability states, ergodic convergence of each constituent transition circuit then implies ergodic convergence of the whole assembly. The only requirement for scheduling transitions is that each circuit must be left fixed while circuits for variables that interact with it are transitioning. This scheduling requirement — that a transition circuit’s value be held fixed while others that read from its internal state or serve as inputs to its next transition are updating — is analogous to the so-called “dynamic discipline” that defines valid clock schedules for traditional sequential logic. Deterministic and stochastic schedules, implementing cycle or mixture hybrid kernels, are both possible. This simple rule also implies a tremendous amount of exploitable parallelism in stochastic transition circuits: if two variables are independently caused given the current setting of all others, they can be updated at the same time.
Assemblies of stochastic transition circuits implement Bayesian reasoning in a straightforward way: by fixing, or “clamping” some of the variables in the assembly. If no variables are fixed, the circuit explores the full joint distribution, as shown in Figure 2E and 2F. If a variable is fixed, the circuit explores the conditional distribution on the remaining variables, as shown in Figure 2G and 2H. Simply by changing which transition circuits are updated, the circuit can be used to answer different probabilistic queries; these can be varied online based on the needs of the application.
(Figure 2 about here.)
The accuracy of ultra-low-precision stochastic transition circuits.
The central operation in many Markov chain techniques for inference is called DISCRETE-SAMPLE, which generates draws from a discrete-output probability distribution whose weights are specified on its input. For example, in Gibbs sampling, this distribution is the conditional probability of one variable given the current value of all other variables that directly depend on it. One implementation of this operation is shown in Figure 3A; each stochastic transition circuit from Figure 2 could be implemented by one such circuit, with multiplexers to select log-probability values based on the neighbors of each random variable. Because only the ratios of the raw probabilities matter, and the probabilities themselves naturally vary on a log scale, extremely low precision representations can still provide accurate results. High entropy (i.e. nearly uniform) distributions are resilient to truncation because their values are nearly equal to begin with, differing only slightly in terms of their low-order bits. Low entropy (i.e. nearly deterministic) distributions are resilient because truncation is unlikely to change which outcomes have nonzero probability. Figure 3B quantifies this low-precision property, showing the relative entropy (a canonical information theoretic measure of the difference between two distributions) between the output distributions of low precision implementations of the circuit from Figure 3A and an accurate floating-point implementation. Discrete distributions on 1000 outcomes were used, spanning the full range of possible entropies, from almost 10 bits (for a uniform distribution on 1000 outcomes) to 0 bits (for a deterministic distribution), with error nearly undetectable until fewer than 8 bits are used. Figure 3C shows example distributions on 10 outcomes, and Figure 3D shows the resulting impact on computing element size. Extensive quantitative assessments of the impact of low bit precision have also been performed, providing additional evidence that only very low precision is required .
(Figure 3 about here.)
Efficiency gains on depth and motion perception and perceptual learning problems
Our main results are based on an implementation where each stochastic gate is simulated using digital logic, consuming entropy from an internal pseudorandom number generator. This allows us to measure the performance and fault-tolerance improvements that flow from stochastic architectures, independent of physical implementation. We find that stochastic circuits make it practical to perform stochastic inference over several probabilistic models with 10,000+ latent variables in real time and at low power on a single chip. These designs achieve a 1,000x speed advantage over commodity microprocessors, despite using gates that are 10x slower. In , we also show architectures that exhibit minimal degradation of accuracy in the presence of fault rates as high as one bit error for every 100 state transitions, in contrast to conventional architectures where failure rates are measured in bit errors (failures) per billion hours of operation.
Our first application is to depth and motion perception, via Bayesian inference in lattice Markov Random Field models. The core problem is matching pixels from two images of the same scene, taken at distinct but nearby points in space or in time. The matching is ambiguous on the basis of the images alone, as multiple pixels might share the same value; prior knowledge about the structure of the scene must be applied, which is often cast in terms of Bayesian inference. Figure 4A illustrates the template probabilistic model most commonly used. The X variables contain the unknown displacement vectors. Each Y variable contains a vector of pixel similarity measurements, one per possible pair of matched pixels based on X. The pairwise potentials between the X variables encode scene structure assumptions; in typical problems, unknown values are assumed to vary smoothly across the scene, with a small number of discontinuities at the boundaries of objects. Figure 4B shows the conditional independence structure in this problem: every other X variable is independent from one another, allowing the entire Markov chain over the X variables to be updated in a two-phase clock, independent of lattice size. Figure 4C shows the dataflow for the software-reprogrammable probabilistic video processor we developed to solve this family of problems; this processor takes a problem specification based on pairwise potentials and Y values, and produces a stream of posterior samples. When comparing the hardware to hand-optimized C versions on a commodity workstation, we see a 500x performance improvement.
(Figure 4 about here.)
We have also built stochastic architectures for solving perceptual learning problems, based on fully Bayesian inference in Dirichlet process mixture models[35, 36]. Dirichlet process mixtures allow the number of clusters in a perceptual dataset to be automatically discovered during inference, without assuming an a priori limit on the models’ complexity, and form the basis of many models of human categorization[37, 38]. We tested our prototype on the problem of discovering and classifying handwritten digits from binary input images. Our circuit for solving this problem operates on an online data stream, and efficiently tracks the number of perceptual clusters this input; see  for architectural and implementation details and additional characterizations of performance. As with our depth and motion perception architecture, we observe over 2,000x speedups as compared to a highly optimized software implementation. Of the 2000x difference in speed, roughly 256x is directly due to parallelism — all of the pixels are independent dimensions, and can therefore be updated simultaneously.
(Figure 5 about here.)
Automatically generated causal reasoning circuits and spiking implementations
Digital logic gates and their associated design rules are so simple that circuits for many problems can be generated automatically. Digital logic also provides a common target for device engineers, and have been implemented using many different physical mechanisms – classically with vaccum tubes, then with MOSFETS in silicon, and even on spintronic devices. Here we provide two illustrations of the analogous simplicity and generality of stochastic digital circuits, both relevant for the reverse-engineering of intelligent computation in the brain.
We have built a compiler that can automatically generate circuits for solving arbitrary causal reasoning problems in Bayesian network models. Bayesian network formulations of causal reasoning have played central roles in machine intelligence and computational models of cognition in both humans and rodents. Figure Automatically generated causal reasoning circuits and spiking implementationsA shows a Bayesian network for diagnosing the behavior of an intensive care unit monitoring system. Bayesian inference within this network can be used to infer probable states of the ICU given ambiguous patterns of evidence — that is, reason from observed effects back to their probable causes. Figure Automatically generated causal reasoning circuits and spiking implementationsB shows a factor graph representation of this model; this more general data structure is used as the input to our compiler. Figure Automatically generated causal reasoning circuits and spiking implementationsC shows inference results from three representative queries, each corresponding to a different pattern of observed data.
We have also explored implementations of stochastic transition circuits in terms of spiking elements governed by Poisson firing statistics. Figure Automatically generated causal reasoning circuits and spiking implementationsD shows a spiking network that implements the Markov chain from Figure Fast Bayesian Inference via Massively Parallel Stochastic Transition Circuits. The stochastic transition circuit corresopnding to a latent variable is implemented via a bank of Poisson-spiking elements with one unit per possible value of the variable. The rate for each spiking element is determined by the unnormalized conditional log probability of the variable setting it corresponds to, following the discrete-sample gate from Figure The accuracy of ultra-low-precision stochastic transition circuits. the time to first spike , with obtained by summing energy contributions from all connected variables. The output value of is determined by , i.e. the element that spiked first, implemented by fast lateral inhibition between the s. It is easy to show that this implements exponentiation and normalization of the energies, leading to a correct implementation of a stochastic transition circuit for Gibbs sampling; see  for more information. Elements are clocked quasi-synchronously, reflecting the conditional independence structure and parallel update scheme from Figure Fast Bayesian Inference via Massively Parallel Stochastic Transition CircuitsD, and yields samples from the correct equilibrium distribution.
This spiking implementation helps to narrow the gap with recent theories in computational neuroscience. For example, there have been recent proposals that neural spikes correspond to samples, and that some spontaneous spiking activity corresponds to sampling from the brain’s unclamped prior distribution. Combining these local elements using our composition and abstraction laws into massively parallel, low-precision, intentionally stochastic circuits may help to bridge the gap between probabilistic theories of neural computation and the computational demands of complex probabilistic models and approximate inference.
(Figure 6 about here.)
To further narrow the efficiency gap with the brain, and scale to more challenging Bayesian inference problems, we need to improve the convergence rate of our architectures. One approach would be to initialize the state in a transition circuit via a separate, feed-forward, combinational circuit that approximates the equilibrium distribution of the Markov chain. Machine perception software that uses machine learning to construct fast, compact initializers is already in use. Analyzing the number of transitions needed to close the gap between a good initialization and the target distribution may be harder. However, some feedforward Monte Carlo inference strategies for Bayesian networks provably yield precise estimates of probabilities in polynomial time if the underlying probability model is sufficiently stochastic; it remains to be seen if similar conditions apply to stateful stochastic transition circuits.
It may also be fruitful to search for novel electronic devices — or previously unusable dynamical regimes of existing devices — that are as well matched to the needs of intentionally stochastic circuits as transistors are to logical inverters, potentially even via a spiking implementation. Physical phenomena that proved too unreliable for implementing Boolean logic gates may be viable building blocks for machines that perform Bayesian inference.
Computer engineering has thus far focused on deterministic mechanisms of remarkable scale and complexity: billlions of parts that are expected to make trillions of state transitions with perfect repeatability. But we are now engineering computing systems to exhibit more intelligence than they once did, and identify probable explanations for noisy, ambiguous data, drawn from large spaces of possibilities, rather than calculate the definite consequences of perfectly known assumptions with high precision. The apparent intractability of probabilistic inference has complicated these efforts, and challenged the viability of Bayesian reasoning as a foundation for engineering intelligent computation and for reverse-engineering the mind and brain.
At the same time, maintaining the illusion of rock-solid determinism has become increasingly costly. Engineers now attempt to build digital logic circuits in the deep sub-micron regime and even inside cells; in both these settings, the underlying physics has stochasticity that is difficult to suppress. Energy budgets have grown increasingly restricted, from the scale of the datacenter to the mobile device, yet we spend substantial energy to operate transistors in deterministic regimes. And efforts to understand the dynamics of biological computation — from biological neural networks to gene expression networks — have all encountered stochastic behavior that is hard to explain in deterministic, digital terms. Our intentionally stochastic digital circuit elements and stochastic computing architectures suggest a new direction for reconciling these trends, and enables the design of a new class of fast, Bayesian digital computing machines.
The authors would like to acknowledge Tomaso Poggio, Thomas Knight, Gerald Sussman, Rakesh Kumar and Joshua Tenenbaum for numerous helpful discussions and comments on early drafts, and Tejas Kulkarni for contributions to the spiking implementation.
Figure 1. (A) Boolean gates, such as the AND gate, are mathematically specified by truth tables: deterministic mappings from binary inputs to binary outputs. (B) Compound Boolean circuits can be synthesized out of sub-circuits that each calculate different sub-functions, and treated as a single gate that implements the composite function, without reference to its internal details. (C) Each stochastic gate samples from a discrete probability distribution conditioned on an input; for clarity, we show an external source of random bits driving the stochastic behavior. (D) Composing gates that sample B given A and C given B yields a network that samples from the joint distribution over B and C given A; abstraction yields a gate that samples from the marginal distribution C—A. When only one sample path has nonzero probability, this recovers the composition of Boolean functions. (E) The THETA gate is a stochastic gate that generates samples from a Bernoulli distribution whose parameter theta is specified via the input bits. Like all stochastic digital gates, it can be specified by a conditional probability table, analogously to how Boolean gates can be specified via a truth table. (F) When each new output sample is triggered (e.g. because its internal randomness source updates), a different output sample is generated; time-averaging the output makes it possible to estimate the entries in the probability table, which are otherwise implicit. (G) The THETA gate can be implemented by comparing the output of a source of (pseudo)random bits to the input coin weight. (H) Deterministic gates, such as the AND gate shown here, can be viewed as degenerate stochastic gates specified by conditional probability tables whose entries are either 0 or 1. This permits fluid interoperation of deterministic and stochastic gates in compound circuits. (I) A parallel circuit implementing a Binomial random variable can be implemented by combining THETA gates and adders using the composition laws from (D).
Figure 2. Stochastic transition circuits and massively parallel Bayesian inference. (A) A deterministic finite state machine consists of a register and a transition function implemented via combinational logic. (B) A stochastic transition circuit consists of a register and a stochastic transition operator implemented by a combinational stochastic circuit. Each stochastic transition circuit is is parameterized by some input , and its internal combinational stochastic block must ergodically converge to a unique stationary distribution for all . (C) Stochastic transition circuits can be composed to construct samplers for probabilistic models over multiple variables by wiring together stochastic transition circuits for each variable based on their interactions. This circuit samples from a distribution . (D) Each network of stochastic transition circuits can be scheduled in many ways; here we show one serial schedule and one parallel schedule for the transition circuit from (C). Convergence depends only on respecting the invariant that no stochastic transition circuit transitions while other circuits that interact with it are transitioning. (E) The Markov chain implemented by this transition circuit. (F) Typical stochastic evolutions of the state in this circuit. (G) Inference can be implemented by clamping state variables to specific values; this yields a restricted Markov chain that converges to the conditional distribution over the unclamped variables given the clamped ones. Here we show the chain obtained by fixing . (H) Typical stochastic evolutions of the state in this clamped transition circuit. Changing which variables are fixed allows the inference problem to be changed dynamically as the circuit is running.
Figure 3. (A) The discrete-sample gate is a central building block for stochastic transition circuits, used to implement Gibbs transition operators that update a variable by sampling from its conditional distribution given the variables it interacts with. The gate renormalizes the input log probabilities it is given, converts them to probabilities (by exponentiation), and then samples from the resulting distribution. Input energies are specified via a custom fixed-point coding scheme. (B) Discrete-sample gates remain accurate even when implemented at extremely low bit-precision. Here we show the relative entropy between true distributions and their low-precision implementations, for millions of distributions over discrete sets with 1000 elements; accuracy loss is negligible even when only 8 bits of precision are used. (C) The accuracy of low-precision discrete-sample gates can be understood by considering multinomial distributions with high, medium and low entropy. High entropy distributions involve outcomes with very similar probability, insensitive to ratios, while low entropy distributions are dominated by the location of the most probable outcome. (D) Low-precision transition circuits save area as compared to high-precision floating point alternatives; these area savings make it possible to economically exploit massive parallelism, by fitting many sampling units on a single chip.
Figure 4. (A) A Markov Random Field for solving depth and motion perception, as well as other dense matching problems. Each node stores the hidden quantity to be estimated, e.g. the disparity of a pixel. Each ensures adjacent s are either similar or very different, i.e. that depth and motion fields vary smoothly on objects but can contain discontinuities at object boundaries. Each node stores a per-latent-pixel vector of similarity information for a range of candidate matches, linked to the s by the potentials. (B) The conditional independencies in this model permit many different parallelization strategies, from fully space-parallel implementations to virtualized implementations where blocks of pixels are updated in parallel. (C) Depth perception results. The left input image, plus the depth maps obtained by software (middle) and hardware (right) engines for solving the Markov Random Field. (D) Motion perception results. One input frame, plus the motion flow vector fields for software (middle) and hardware (right) solutions. (E) Energy versus time for software and hardware solutions to depth perception, including both 8-bit and 12-bit hardware. Note that the hardware is roughly 500x faster than the software on this frame. (F) Energy versus time for software and hardware solutions to motion perception.
Figure 5. (A) Example samples from the posterior distribution of cluster assignments for a nonparametric mixture model. The two samples show posterior variance, reflecting the uncertainty between three and four source clusters. (B) Typical handwritten digit images from the MNIST corpus, showing a high degree of variation across digits of the same type. (C) The digit clusters discovered automatically by a stochastic digital circuit for inference in Dirichlet process mixture models. Each image represents a cluster; each pixel represents the probability that the corresponding image pixel is black. Clusters are sorted according to the most probable true digit label of the images in the cluster. Note that these cluster labels were not provided to the circuit. Both the clusters and the number of clusters were discovered automatically by the circuit over the course of inference. (D) The receiver operating characteristic (ROC) curves that result from classifying digits using the learned clusters; quantitative results are competitive with state-of-the-art classifiers. (E) The time required for one cycle through the outermost transition circuit in hardware, versus the corresponding time for one sweep of a highly optimized software implementation of the same sampler, which is 2000x slower.
Figure 6. (A) A Bayesian network model for ICU alarm monitoring, showing measurable variables, hidden variables, and diagnostic variables of interest. (B) A factor graph representation of this Bayesian network, rendered by the input stage for our stochastic transition circuit synthesis software. (C) A representation of the factor graph showing evidence variables as well as a parallel schedule for the transition circuits automatically extracted by our compiler: all nodes of the same color can be transitioned simultaneously. (D) Three diagnosis results from Bayesian inference in the alarm network, showing high accuracy diagnoses (with some posterior uncertainty) from an automatically generated circuit. E) The schematic of a spiking neural implementation of a stochastic transition circuit assembly for sampling from the three-variable probabilistic model from Figure 2. (F) The spike raster (black) and state sequence (blue) that result from simulating the circuit. (G) The spiking simulation yields state distributions that agree with exact simulation of the underlying Markov chain.
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