No Multiplication? No Floating Point? No Problem! Training Networks for Efficient Inference
For successful deployment of deep neural networks on highly–resource-constrained devices (hearing aids, earbuds, wearables), we must simplify the types of operations and the memory/power resources used during inference. Completely avoiding inference-time floating-point operations is one of the simplest ways to design networks for these highly-constrained environments. By discretizing both our in-network non-linearities and our network weights, we can move to simple, compact networks without floating point operations, without multiplications, and avoid all non-linear function computations. Our approach allows us to explore the spectrum of possible networks, ranging from fully continuous versions down to networks with bi-level weights and activations. Our results show that discretization can be done without loss of performance and that we can train a network that will successfully operate without floating-point, without multiplication, and with less RAM on both regression tasks (auto encoding) and multi-class classification tasks (ImageNet). The memory needed to deploy our discretized networks is less than of the equivalent architecture that does use floating-point operations.
We address this in two steps. First, we train deep networks that emit only a predefined, static number of discretized values. Despite reducing the number of values that can be emitted from to only 32, there is little to no degradation in network performance across a variety of tasks. Compared to existing approaches for discretization, our approach is both conceptually and programmatically simple and has no stochastic component. Second, we provide a method to constrain the network’s weights to a small number of unique values (typically 100-1000) by employing a periodic adaptive clustering step during training. With only weight clustering in place, large network models can be transmitted (or stored) using less than a fourth of the bandwidth.
Almost all recent neural-network training algorithms rely on gradient-based learning. This has moved the research field away from using discrete-valued inference, with hard thresholds, to smooth, continuous-valued activation functions Werbos (1974); Rumelhart et al. (1986). Unfortunately, this causes inference to be done with floating-point operations, making it difficult to deploy on an increasingly-large set of low-cost, limited-memory, low-power hardware in both commercial Lane et al. (2015) and research settings Bourzac (2017).
Avoiding all floating point operations allows the inference network to realize the power-saving gains available with fixed-point processing Finnerty and Ratigner (2017). To move fully to fixed point, we need to discretize both the network weights and the activation functions. We can also achieve significant memory savings, by not just quantizing the network weights, but clustering all of them across the entire network into a small number of levels. With this in place, the memory footprint grows about (or less) as fast as the unclustered, continuous-weight network size. Additionally, the relative rates at which our memory footprint will grow is easily controlled using , the number of unique weights. In our experiments, with , we show that we can meet or exceed the classification performance of an unconstrained network, using the same architecture and (nearly) the same training process.
While most neural networks use continuous non-linearities, many use non-linearities with poorly defined gradients, without impacting the training process. Goodfellow et al. (2013); Glorot et al. (2011); Nair and Hinton (2010). When purely discrete outputs are desired, however, such as with binary units, a number of additional steps are normally taken Raiko et al. (2014); Bengio et al. (2013a); Hou et al. (2016); Courbariaux et al. (2016); Tang and Salakhutdinov (2013); Maddison et al. (2016) or evolutionary strategies are used Plagianakos et al. (2001). At a high level, many of the methods employ a stochastic binary unit and inject noise during the forward pass to sample the units and the associated effect on the network’s outputs. With this estimation, it is possible to calculate a gradient and pass it through the network. One interesting benefit of this method is its use in generative networks in which stochasticity for diverse generation is desired Raiko et al. (2014). Raiko et al. (2014) also extended Tang and Salakhutdinov (2013) to show that learning with stochastic units may not be necessary within a larger deterministic network.
A different body of research has focused on discretizing and clustering network weights. Wu et al. (2018); Yi et al. (2008); Deng et al. (2017); Courbariaux et al. (2016) Several existing weight-quantization methods (e.g., Courbariaux et al. (2016)) liken the process to Dropout Srivastava et al. (2014) and its regularization effects. Instead of randomly setting activations to zero when computing gradients (as with dropout), weight clustering and binarization tends to push extreme weights partway back towards zero. Additional related work is given in the next section.
2 Training Networks for Efficient Inference
In this section, we separately consider the tasks of (1) discretizing the output of each unit and (2) reducing the set of allowed weights to a small, preset, size. The effects of each method are examined in isolation and together in Section 3.
2.1 Discretizing the Network’s Activations
To make this section concrete and easily reproducible, we will focus our attention on how to discretize the tanh activation function. However, we have employed the exact same method to discretize ReLU-6 Krizhevsky and Hinton (2010), rectified-Tanh, and sigmoid among others. Figure 1 gives a simple procedure that we use for activation discretization and shows its effects on the activation’s output.
Naively backpropagating errors with these discretized tanh (tanhD) units will quickly run into problems as the activations are both discontinuous and characterized by piece-wise constant functions. In order to use gradient based methods with tanhD units, we need to define a suitable derivative. We simply use the derivative of the underlying function — (e.g. for , we used ). In the forward pass, both in training and inference, the output is discretized to levels. In the backward pass, we proceed by ignoring the discretization and instead compute the derivatives of the underlying function. Whereas previous studies that attempted discretization to binary-output units experienced difficulties in training, we have found that as is increased, even to relatively small values (), all of the currently popular training algorithms perform well with no modification Baluja (2018), (e.g. SGD, SGD+momentum, ADAM, RMS-Prop, etc). A number of studies have used similar approaches, often in a binary setting (e.g. straight through estimatorsHinton (2012); Bengio et al. (2013b); Rippel and Bourdev (2017)); most recently, Agustsson et al. (2017); Mentzer et al. (2018) used a smooth mixture of the quantized and underlying function in the backwards pass.
Why does ignoring the discretization in the backwards pass work? If we had tried to use the discretized outputs, the plateaus would not have given usable derivatives. By ignoring the discretizations, the weights of the network still move in the desired directions with each backpropagation step. However, unlike non-discretized units, any single move may not affect the unit’s output. In fact, it is theoretically possible the entire network’s output may not change despite all the weight changes made in a single step. Nonetheless, in a subsequent weight update, the weights will again be directed to move, and of those that move in the same direction, some will cause a unit’s output to cross a discretization threshold. This changes the unit’s and, eventually, the network’s output. Further, notice that for tanhD, Figure 1, the plateaus are not evenly sized. Where the magnitude of the derivative for the underlying tanh function is maximum is where the plateau is the smallest. This is beneficial in training since the unit’s output changes most rapidly where the derivative of tanh changes the most rapidly.
Finally, to provide an intuitive example of how these units perform in practice, see Figure 2. This shows how a parabola is fit with a variety of activations and discretization. In this example, a tiny network with a single linear output unit and only two hidden units is used. The most revealing graphs are the training curves with tanhD() (Figure 2-c). The fit to the parabola matches closely with intuition; the different levels of discretization symmetrically reduce the error in a straightforward manner. As is increased (d and e), the performance approximates and then matches the networks trained with tanh and ReLU activations.
To summarize, a simple procedure to discretize the outputs of a unit’s activations during inference and training was given. For ease of exposition and clarity, it was presented with tanh, though testing has been done with most, if not all, the commonly used activation functions. Beyond tanhD, we will demonstrate the use of discretized ReLU activations in Section 3.
2.2 Weight Enumeration
We turn now to reducing the set of allowed weights to a small, predefined, number. As the goal of our work is efficient inference; we do not attempt to stay discretized during the training process. The process used to obtain only a small number of unique weights is a conceptually simple addition to standard network training. Like activation discretization, it can be used with any weight setting procedure - from SGD or ADAM to evolutionary algorithms.
Previous research has been conducted in making the network weights integers Wu et al. (2018); Yi et al. (2008), as well as reducing the number of weights to only binary or ternary values Deng et al. (2017); Courbariaux et al. (2016) during both testing and training, using a stochastic update process driven by the sign bit of the gradient. Empirically, many of the previous techniques that either compress or quantize weights on an already trained model perform poorly on real-valued regression problems. While our implementations of these methods Denton et al. (2014); Han et al. (2016); Lin et al. (2015) are quite successful on classification problems, we were unable to achieve comparable performance using those techniques on networks that perform image compression and reconstruction. Discretizations can create sharp cuts that seem to be beneficial for decision boundaries but hinder performance when regressing to real-valued variables. Tasks in which real-valued outputs are required have become common recently (e.g. image-to-image-translation Isola et al. (2017), image compression and speech synthesis, to name a few). Fortunately, our method exhibits good performance on regression tasks, as well as providing an easily tunable hyper-parameter (the number of weight clusters), thereby alleviating any remaining task impact.
Perhaps the most straightforward approach to creating a network with only a limited number of unique weights () is to start with a trained network and place each weight into a small number, , of equally sized bins that span the full range of weight values. Each weight is then assigned the centroid of that bin. This approach has limitations: (1) the network needs to be retrained after this procedure. However, continuing training re-introduces small deviations from the centroids and therefore the network once again has a large number of weights. (2) The distribution of weights is far from uniform, see Figure 3 (top row). (3) If we decrease the fidelity of the relatively few large magnitude weights, we have observed severely degraded performance across a wide variety of tasks.
To address these limitations, we adaptively cluster the weights
throughout the training process. Rather than fully training a
network and then discretizing the weights, a recurring clustering step
is added into the training procedure. Periodically, all of the
weights in the network (including the bias weights) are added to a
bucket from which clusters are found. This is a one-dimensional
clustering problem, where the single dimension is the value of the
This procedure, though simple, has some subtle effects. First, as a training regularizer, it keeps the range of the weights from growing too quickly, as there is a persistent “regression to the mean.” Second, it provides a mechanism to inject directed noise into the training process. As we will show, both of these properties have, at times, yielded improved results over allowing arbitrary valued weights. Figure 3 (row b) shows the distribution of weights at the beginning, middle, and end of training when weight clustering is used, immediately prior to the quantization step. With 1000 clusters used throughout training, the weights after replacement (Figure 3, row c) appear very similar to the unclustered weights (Figure 3, row b).
An extensive exploration of tasks to elucidate how discretizations of activations and weights affected the performance was conducted. These included testing memorization capacity, real-value function approximation, and numerous classification problems. Because of space limitations, however, we only present the three most often researched tasks; these are representative of the results seen across our studies. We present two classification tasks: MNIST LeCun et al. (1998), and ImageNet Russakovsky et al. (2014) and a real-valued output task: auto-encoding images, the crucial building block to neural-network based image compression.
In all of our tests, we retrained the baselines to eliminate the possibility of any task-specific heuristics. In some cases, this led to lower performance than state-of-the-art; however, since our goal is to measure the relative effect of discretizations on any network, the results provide the insights needed.
For MNIST, we train a fully connected network with ADAM Kingma and Ba (2015) and vary the number of hidden units to explore the trade-off between discretization, accuracy, and network size. Figure 4-a contains the performance of the networks using ReLU and tanh activation functions with no discretizations; these are the baselines. Since tanh slightly outperformed ReLU, we will discretize tanh in our experiments.
First, we examine the effect of only discretizing each unit’s activations. We experimented with 8 sets of activation discretization (2, 4, 8, … 256 levels). We found that both tanhD(8) and tanhD(16) often perform as well as tanh and ReLU in performance when there are hidden units per layer. At tanh(32) and above, performance is largely indistinguishable from tanh (Figure 4-a). Next, we examine weight discretization in isolation. Two sets of experiments are presented: and (Figure 4 b). With 1000 unique weights allowed, the performance is nearly identical to no weight quantization. However, when is reduced to 100, there is a decline in performance. Nonetheless, note that even with the performance recovers with additional hidden units – hinting towards the trade-off in representational capacity between number of distinct values a weight can represent and the number of weights in the network.
Finally, when both discretizations are combined, we again see that the only noticeable negative effect comes when the number of unique weights is set to 100. No matter which activation is used, when 100 weights are used, performance decreases. This same trend holds true for networks with a depth of 2 hidden layers (top row) and with 4 hidden layers (bottom row).
A number of recent as well as classic research papers have used auto-encoding networks as the basis for image compression Jiang (1999); Toderici et al. (2016); Cottrell and Munro (1988); Kramer (1991); Toderici et al. (2017); Svoboda et al. (2016). To recreate the input image, real values are used as the outputs. As discussed earlier, real value approximation can be a more challenging problem domain than classification when discretizing the network.
For these experiments, we train two network architectures, convolutional and another with only fully connected layers. ADAM is used for training, and error is minimized. We trained with the ImageNet train-set and all tests are performed with the validation images. The smallest conv-network has four conv. layers with (, , , ) depth, followed by 3 conv-transpose layers with depth (, , ). The last two layers are conv. with depth 20 and 3. For the second experiment, the fully-connected network has 7 hidden layers with (, , , , , ) units each. To examine the effects of network size, is varied from 0.5 to 2.0 for both experiments.
Because the raw numbers are not meaningful in isolation, we show performance measurements relative to training the smallest network with ReLU activations and no quantizations (the graphs for both architectures can be compared to see effects of network size). In Figure 5-a for both architectures, ReLU performed worse than tanh. TanhD(32) and TanhD(256) tracked the performance of tanh closely for all sizes of the network. Similarly to the MNIST experiment (Figure 4 b), reducing the number of weights to hurt performance. With , the performance decline was much smaller; however, unlike with MNIST, there was a discernible effect.
When the two discretizations were combined, again, the largest impact was a result of setting the weight discretization levels too low. As before, increasing the network size returns the performance lost due to weight and activation quantization. Importantly, this task indicates that although the discretization procedures do indeed take a larger toll on the performance with real-outputs, discretization remains a viable approach for network computation/memory reduction. The amount of performance degradation tolerated can be explicitly dictated by the needs of the application by controlling .
To evaluate the effects of discretization on a larger network, we used AlexNet Krizhevsky et al. (2012) to address the 1000-class ImageNet task. To ensure that we are training our network correctly, we first retrained AlexNet from scratch using the same architecture and training procedures specified in in Krizhevsky et al. (2012); some small differences are: we employed an RMSProp optimizer, weight initializer sd=0.005, bias initializer sd=0.1, one Nvidia Tesla P100 GPU, and a stepwise decaying learning rate. Our network achieved a recall@5 accuracy of 80.1% and recall@1 accuracy of 57.1%. This should be compared to the accuracy reported in Krizhevsky et al. (2012) of 81.8% and 59.3%, recall@5 and recall@1, respectively. The small difference in performance was because we did not use the PCA pre-processing, which Krizhevsky et al. (2012) cite as causing approximately the difference seen. This performance was achieved by including training with crops and flips and using the average of multiple forward-passes with random crops during evaluation.
With the above matching performance, we were confident that our training approach matches the AlexNet one sufficiently. However, as we needed to speed-up training to explore the parameter space we were interested in thoroughly, we eliminated using multiple crops in training and testing and retrained the AlexNet system. This system is used as as our baseline. The baseline (AlexNet without crops and resizes) achieved a recall top-1 of 49.6% and top-5 of 74.2%.
To begin, we examined the effect of switching to ReLU6 instead of ReLU. AlexNet with ReLU6 achieved a recall top-1 of 47.8% and top-5 of 72.8% (Table 1, Experiment #1). All of the remaining comparisons will use the exact same training procedure, only differing in which discretizations and activations are used.
Following our experimental design, we first independently examine the performance of only discretizing each unit’s activations, see Table 1. With 256 activation levels (8 bits) down to only 32 levels (5 bits) (Experiments #2 and #3), there is little degradation in performance in comparison to using the full 32 bits of floating point (Experiment #1). As expected, as the activation levels become more sparse, performance declines (Experiments #4 and #5).
Using the most aggressive acceptable discretization (32 values), we
turn to our next experiment: reducing the number of unique weights
allowed. We set (Experiment #6). The only training
modification was the elimination of dropout. As illustrated in
Figure 3, the discretization
process itself works as a regularizer, so dropout is not needed.
Wu et al. (2018) took a similar approach and removed
dropout from their AlexNet discretization
Examining Experiment #6, there is actually an increase in performance in both recall@1 and recall @5 from baseline AlexNet-ReLU6 (Experiment #1). The results even overcame the slight penalty of using ReLU6 instead of ReLU (Experiment #0). Further, Experiment #7, with only 100 unique weights, performed much better than we would have expected given its earlier performance. We speculate that unlike the other tasks in which setting was detrimental to performance, AlexNet has so much extra capacity and depth that the effective decrease in representational capacity for each weight was lessened by the large architecture. We will return to this later.
|AlexNet - Full Training & Testing (w/crops + rotations)||-||-||57.1||80.1|
|AlexNet w/ ReLU & simplified training & testing||0||-||-||49.6||74.2|
|AlexNet w/ ReLU6 & simplified training & testing||1||-||-||47.8||72.8|
|Continuous weights, only discretized activations.||2||256||-||47.0||72.4|
|k-Means discretized weights and discretized activations (no dropout).||6||32||1000||49.6||74.7|
|Laplacian discretized weights, discretized activations:|
|- with dropout||8||32||1000||47.4||72.3|
|- without dropout||9||32||1000||51.7||75.7|
|Laplacian discretized weights, discretized activations|
|(Exp #9) with full training||10||32||1000||55.7||79.3|
The results to this point show minimal loss in performance (relative to Experiment #1) after the activation is discretized to 32 levels and 1000 weights are used. This improves on the relative loss seen in earlier studies Zhou et al. (2018); Wu et al. (2018); Hubara et al. (2016). Nonetheless, as a final test, we ask whether it is possible to do better? The AlexNet network contains approximately 50 million weights. Unconstrained clustering using all of the weights is computationally expensive, slowing our training process. However, sampling the weights for modeling leads to degraded performance as the large-magnitude weights may not be accurately represented. Here, we briefly outline our final experiment: attempting to replace the clustering procedure with an approximation of what it should do, based on our observations of its behavior across all problems. This approximation was guided by the quantization levels that we see in the k-Means clustering: as shown in Figure 3-c, the distribution of unconstrained cluster centers converges to an approximation of a truncated Laplacian distribution.
Can we force the quantization levels into a Laplacian-like distribution? We do this by setting normalized levels at where and , up to . The actual quantization levels are then set to where is the mean value of the network weights and is a scaling factor. Our original approach was to set where is the maximum amplitude difference between any weight and the mean . This scaling allows us to accurately model the largest magnitude weights. However, when we do that, we lose the regularization benefits seen in Figure 3-b and -c. Instead, we “nudge” the value of just slightly lower, to adjust downward by , whenever the activation weights are spread out by more than the expected range of desired values (specifically, whenever ). We have found that we can also speed up training by providing a similar “nudge” larger to the value of , when the activation weights are clustered too tightly around there mean: specifically, if , we change to move outward by
We have seen an improvement to performance using this Laplacian discretization (Experiment #9). We surpass the performance both of our fully continuous baseline (Experiment #1) and of our k-Means weight clustering (Experiment #6). Finally, in Experiment #10, we repeated Experiment #9 but, instead of using the simplified training (as Experiments #0-#9 do), we used the “full training” process, including crops and rotations in training and testing. With that full training, we regain nearly all of the accuracy of the original Alexnet. In fact, the drop in accuracy from the original Alexnet to Experiment #10 is less than the drop in accuracy from Experiment #0 to Experiment #1, where the only change we made was replacing the ReLU with a ReLU6 (without any quantization). Whether the performance benefit continues is an exciting avenue for future work; however, avoiding the computational expense of clustering is already pragmatically very beneficial.
|Top 1 Accuracy||Top 5 Accuracy|
|DoReFa Zhou et al. (2018)||55.9%||53.0%||-2.9%||-||-||-|
|WAGE Wu et al. (2018)||-||-||-||80.7%||72.2%||-8.5%|
|QNN Hubara et al. (2016)||56.6%||51.3%||-5.3%||80.2%||73.7%||-6.5%|
Compared to the prior work that focused on moving away from floating-point implementations (Table 2), our approach is the only one which did not suffer a significant loss in performance, relative to the unquantized version of the network. We have, by far, the best performance both relative to baseline and in absolute terms. Han et al. (2016) is the only other reference that we have found with weight quantization that did not suffer from performance but Han et al. (2016) does not quantize activations and requires floating point calculations. DoReFa Zhou et al. (2018), which is the closest in performance to ours, is 8 times slower than the baseline implementation, whereas we expect our implementation to be faster than the baseline, due to the relative speed of lookups versus multiplies.
4 Memory Savings, No Multiplication, No Floating Point
As shown, it is possible to train networks with little (if any) additional error introduced through discretizing the activation function. On top of the discretized activation function, we can use our clustering approach to reduce the number of unique weights in the network. With these two discretization components in place, an inference step in a fully trained neural network can be completed with no multiplication operations, no non-linear function computation, and no floating point representations.
To accomplish this,
we discretize the non-linear
activation function to activations and allow unique
weights in the network. We pre-compute all of the multiplications
required and store them in a table of size . In our
AlexNet experiments, we typically used , which required
storing 32,000 entries. However, this extra memory requirement is
completely offset with the savings obtained from no longer needing to
store the weights. Previously, for each weight, a floating point
number (32 bits) was required. With this method, only an index to the
correct column in the table is needed (10 bits). Given
the number of weights in a network like AlexNet (),
this reflects savings in memory, in addition to computational
savings detailed below. Furthermore, in terms of bandwidth for
downloading trained models (for example to mobile devices) we can find
greater efficiency by using entropy coding of the weight indices:
based on our fully-trained discrete network weight distributions, even
the simplest (non-adaptive, marginal-only) entropy coding reduces the
index size from 10 bits to below 7 bits, giving a savings in
model storage size.
We replace floating point computations with table lookups and integer summation. The first step is to replace the weight multiplication by a look-up into the table described above, with the refinement of representing the result (the looked-up value) using a fixed-point representation Wiki (2018). As long as our weights are within a known, bounded range (which they are since we know our cluster-center values) and as long as the previous layers outputs are in a known, bounded range (which they are due to our bounded quantization), we can always pick a scale factor that keeps the result from overflowing our fixed-point representation. As we look up the values, we simply sum them together, with integer summation. The summed values are then mapped to the output value of the activation using a look up, that is an index into the row of the table. Also, note that in the unlikely event that longer integers are required in the tables, increasing bits here is a minor expense in comparison to storing full resolution weights.
4.1 Implementation Details
In this section, we provide suggestions to make the implementation
more efficient. In the first example, shown in
Figure 6, we show how a pre-computed activation
multiplication table can be deployed. In the example, we
show a single unit with a network; the unit has 4 inputs + a bias
unit. Note that the stored multiplication table also has a row for
the bias unit’s computation (e.g. multiplying the bias unit’s weight
by an “activation” of 1.0). Note that the same multiplication table
is used across all of the network’s nodes.
Once the summation is computed by adding the looked-up entries from the multiplication table, the activation is computed on the summed value. In this example, the activation is shown with a Relu-6 activation function with 6 levels of discretization. There are two remaining inefficiencies in this system. First, in terms of memory, we represented all the stored values in floating point. Second, though we do not compute the activation function’s output, finding the right output one of , requires that we examine the boundaries of the bins (). This scan (whether binary or linear) must exist if we want to truly have inference without any multiplications (or, equivalently, division operations).
To address the first issue, we switch to a fixed-point / integer representation for all the stored values. This is shown in Figure 7. Note that all the values are multiplied by a large scale-factor, , before they are converted to integers and stored in the table. must be large enough to push the important decimals to values > 1. The easiest method of selecting is empirically, as having too large is not detrimental as long as the additions fit in the allocated memory for the temporary accumulator variables required. Note that even if long-ints are required (in most cases they will not be), the amount of memory devoted to the table and associated accumulators is minuscule in comparison to the amount of memory required to store all the weights. Note that the bias unit’s effect, which is actually a , must now also be scaled to the appropriate range, and is therefore changed to . The activation function now emits an integer/fixed point number that is scaled by . Finally, note that the boundaries for the activation function must be adjusted by since both the weights and activation functions are scaled when entered/looked up in the table.
To address the second inefficiency, requiring a scan of the boundaries, , in the activation function, we directly look-up the bin in the activation that contains the correct output, see Figure 8. First, note that we set to be a large power of 2, , (e.g. ). This in contrast to the more common selection of large scale factor that are often chosen to be a power of 10. By sacrificing easy human-readability, we enable faster operations. As shown, all of the same computations are conducted for the multiplication table, and, as before, the results are stored as fixed-point / integers. Instead of requiring a scan of the boundaries in the activation function, after the summation of the inputs is computed, it is bit-shifted . In this example, it removes the least significant 48 bits (). Once these bits are removed, the result can be interpreted as the index of the “bin” to look in to find the output of the activation function. The bit-shifting operation is faster than a linear (or binary) scan of the boundaries and faster than general multiplication and divide operations.
One of the limitations of this lookup-approach is that it works in cases in which the spacing between the activation boundaries is uniform. In the ReLU-6 activations, the size of the output bins was kept constant; however, when we discretized the tanh activation (Section 2.1), we noted that the width of the bins varied. Using this approach with a tanh non-linearity necessitates retraining with constant size boundaries for the activation discretization. Though achievable, this would eliminate one of the advantages that led to faster training – that the size of the bins corresponded well to the derivatives of the underlying function. This had allowed faster moves between the discrete activation bins in the regions where the underlying activation had the largest magnitude derivatives.
In summary, the final approach, shown in Figure 8, accomplishes what we set out to do at inference time: (1) eliminate multiplications in inference. (2) All of the values inference are represented as integers or fixed point decimal; no floating point is used. (3) We have made the activation computation fast; no non-linearities are computed and no scanning of an array is required to avoid multiplies; by a judicious setting for , we were able to use a fast bit-shift operation instead.
Note that during training, floating point is used. Wu et al. (2018) addresses training with integers with various classification problems. Our goal is to ensure that networks, even if trained on the fastest GPUs, can be deployed on small devices that may be constrained in terms of power, speed or memory. Additionally, for our requirements, which encompass deployment of networks outside of the classification genre, we needed the discretization techniques to work with regression/real-valued outputs.
5 Discussion & Future Work
The need to enable more complex computations in the enormous number of deployed devices, rather than sending raw signals back to remote servers to be processed, is rapidly growing. For example, auditory processing within hearing aids, vision processing in remote sensors, custom signal processing in ASICs, or any of the recent photo applications running on the low and medium-powerful cell phones prevalent in many developing countries, all will benefit from on-device processing.
Pursuing discretized networks has led to a number of interesting questions for future exploration. Four immediate directions for future work are presented below. We also use this opportunity to discuss some of the insights/trends we noticed in our study but were not able to discuss fully here.
For discretizing weights, all of the network’s weights were placed into a single bucket. An alternative is to cluster the weights of each layer, or set of layers, independently. If there are distribution differences between layers, this may better capture the most significant weights from each layer.
Currently, is kept constant throughout training. However, we have witnessed instability in the beginning of training quantized weights, especially when is small. These spikes in the training loss dissipate as training progresses. Starting training with a larger-than-desired and gradually decreasing it may address the initial instability.
In many problems, we have seen weight distributions that appear Laplacian. Exploring the use of explicit Laplacian (or other parameterized) models rather than the parameter-less clustering approach is an immediate direction for future work. Preliminary results were quite promising, as shown by Experiments #8 and #9 in Table 1.
We, and other studies, have noticed the regularization-type effects of these methods. Additionally, we have noticed improved performance when other regularizers, such as Dropout, are not used. The use of these methods as regularizers was not explored here and is open for future work.
Beyond the practical ramifications of these simplified networks, perhaps what is most interesting are the implications of the simplified networks on network capacity. In general, we have embraced training ever larger networks to address growing task complexity and performance requirements. However, if we can obtain close performance using only a small fraction of the representational power in the activations and the weights then, with respect to our current models, much smaller networks could store the same information. Why does performance improve with larger networks? Perhaps the answer lies in the pairing of the network architectures and the learning algorithms. The learning algorithms control how the search through the weight-space progresses. It is likely that the architectures used today are explicitly optimized for the task and implicitly optimized for the learning algorithms employed. The large-capacity, widely-distributed, networks work well with the gradient descent algorithms used. Training networks to more efficiently store information, while maintaining performance, may require the use of alternate methods of exploring the weight-space.
- While we have not yet tested 1D-specific clustering Jenks (1967); Wang and Song (2011), all of the approaches we tried (e.g., LVQ Kohonen (1995), k-means Jain (2010), HAC Duda et al. (1995)) gave similar results. We settled on 5 simple lines of code, with scipy scipy.org (2018).
- Further, Wu et al. (2018) did not discretize the last layer for reporting results. All of our discretized AlexNet results include discretization of the final layer.
- This same memory/bandwidth savings is available as soon as the weights are clustered, even if the activations are not discretized.
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