Summary and Contributions: This paper presents NN architectures in which the basic units of information and performed operations are not scalar values and linear translations/ nonlinear activations but rather binary streams (representing real numbers in the range [0,1] as the fraction of times their bit is 'on') and "weighted linear finite state machines" (WLFSMs), which convert between binary streams. The conversion between values using these machines is more expressive than simple linear transformations, for instance, they may also compute tanh or exponents. The authors compute the derivative of the outputs of WLFSMs with respect to their inputs, enabling connecting them into a multi-layer architecture through which backpropagation can be applied. The authors describe how to connect these WLFSMs into architectures for both image processing (which they evaluate on MNIST) and temporal tasks (evaluating on the Penn Treebank). They show that the amount of memory needed for training these architectures is much lower, making training much lighter, and enabling longer-range dependencies.
Strengths: The paper derives backpropagation for WLFSMs, enabling connecting them into novel 'NN' architectures in which the base computation is not linear transformations but rather the (different) range of computing power enabled by WLFSMs. I am very curious whether this will find a different range of expressive power, possibly enabling computation of functions previously out of our range. Additionally, the paper claims much lighter backpropagation for a sequence-processing architecture that they have built for these models, this would be very beneficial if it turns out to also be practical (i.e., train well in practice).
Weaknesses: The exact construction of the architectures from the WLFSM units is hard to understand, and I am unclear on how image or sequence data is expressed and passed into the presented models (and the appendix does not clarify this). Hopefully this can be fixed with clarifications from the authors, as the results do seem exciting if the claims about number of computations/memory needed for training are correct. A sketch of each of the architectures and how they apply to image and temporal data (respectively) would help immensely. I elaborate more in the 'clarity' section. In general, the paper is rather difficult for me, though admittedly I have no experience with WLFSMs or stochastic computing.
Correctness: The derivation of the backpropagation seems correct and the trick using the inverse and multiple qualities of the derivatives/weights was even quite cool, though I feel unequipped to verify it perfectly. I have not understood the exact way in which the model processes images or sequences and so cannot tell if the construction is 'reasonable'.
Clarity: The paper took some passes to understand, though this may be because I am not familiar with stochastic computing. Specific notes: 1. It would especially help to more clearly describe the architectures used for image processing and for sequence processing, especially the latter. An image showing the inputs, outputs (and their 'types') and the connection between the different WLFSMs would be very helpful. In particular, I am still unclear on how exactly the input for a sequence is encoded: suppose I am trying to pass in the character-level sequence 'abc'. What is passed into the network? An example, even a toy architecture, would be really helpful. 2. Additionally it is not entirely clear how bit streams are pooled (lines 124-125 seem to suggest that the stream may take on values other than 1 or 0 at each step, which seems confusing to me?. As a concrete question: if one WLFSM 'A' is receiving input from 3 other WLFSMs, and at some time step t two of the input WLFSMs have transmitted '1' and the other has transmitted '0', what is passed into 'A'? (is it '1', '0', '2/3', or a randomly (uniformly) sampled bit from one of the streams, eg '1' with probability 2/3 and '0' with probability 1/3)? In particular if two streams are being passed into another WLFSM and one of them is transmitting '1' 100% of the time, will that 'drown out' the information in the other? 3. For the temporal tasks, it seems very odd to have a model whose backpropagation/update is based on the current time step only. How can a 'meaningful' state transition (i.e. one in which the state learns to encode useful information from past occurences) be encouraged in this setup? 4. This paper seems to constantly be converting between values in the range (0,1) and (-1,1), 'littering' the equations with remnants of these conversions ((x+1)/2 instead of x, etc). Is it possible to somehow normalise all discussed values at the beginning, so that the remainder of the paper only deals with values in one range? It would make it much easier to follow. 5. My understanding is that WLFSMs essentially compute functions from the range (0,1) to the range (0,1). It does seem that these functions are more expressive than linear transformations (eg lines 105-109), but it would help to explain why it is valuable to use the binary-stream representation for these fractions as opposed to directly passing them into and out of the WLFSMs? (Maybe this is what happens in the implementation in practice?)
Relation to Prior Work: I am not familiar enough with WLFSMs and surrounding literature to judge the relationship of this work to others. With respect to temporal data, it would probably be better to compare to a standard implementation of an LSTM (eg, that provided by the pytorch library). For the comparison to MNIST, it might also have been worthwhile to note a comparison to other NN-based implementations in addition to SC-based ones. These comments are not critical, just suggestions.
Additional Feedback: For the sequence-processing model, you frequently note that the update/computation are based only on current state, making your model much lighter than RNN (which has to backpropagate through multiple time steps). Does this mean the state-update of the model is never learned, i.e., the state transitions of this model are fully deterministic, and it is only the outputs of each state are being learned? If so, is there not a risk that a single state may be reached multiple times by different prefixes, on which it has to learn very different behaviour? Otherwise -- i.e., if the state transition is being learned -- then how is the model learning this without backpropagating through time? I would really appreciate clarifications on this. ==== I have read the author response and appreciate the clarifications. For presentation I wonder if it may still be cleaner to discuss everything in 0/1 or -1/1 exclusively, and note the parts of the architecture that would undergo conversion in practice.
Summary and Contributions: This paper presents a method that can train a multi-layer FSM-based network. Furthermore, they developed an FSM-based model to handle time series data. Compared with LSTM, this method only requires 1/L memory storage and effectively reduces the power consumption when training on GPU.
Strengths: 1) Compared with LSTM, this model dose a better work both in memory usage and test accuracy. 2) This model can directly update via backpropagation 3) Sufficient theoretical elaboration and analysis.
Weaknesses: In addition to LSTM, have you compared your method with others?
Relation to Prior Work: yes
Additional Feedback: UPDATE: After reading the rebuttal, only part of concerns have been resolved. So, I still keep my point.
Summary and Contributions: - Introduce a method to train multi-layer Finite State Machine networks. - Prove the invertibility of a derived function (from FSM steady state conditions), allowing them to compute derivatives. - Demonstrate that these models can: - Synthesize multi-input complex functions (2D Gabor filters) - Outperform SC counterparts of the same size with only half the operations required. - Perform Image classification tasks that require holding a representation of partial input ‘in memory’ - Process and perform well on time-series and sequential data: Character Level Language Modeling on Penn Treebank, War and Peace, and Linux Corpus - Demonstrate computational pros of these models: - No multiplication, only require look-up tables at inference time for the simpler models. - For time series, only requires back propagating gradients for the current input time step, making memory requirements O(1) in sequence length. - Reduces power consumption vs. LSTMs of the same ‘size’ by 33% - Reduces number of operations required for inference by 7x
Strengths: Proofs of validity for gradient computations. Empirical evaluations demonstrate improvement over other stochastic-computing counterpart models. Empirical evaluations demonstrate such models can achieve good performance on temporal-MNIST Empirical evaluations demonstrate similar performance to similarly sized single-layer LSTMs on character level language modeling across several corpora. Both empirical evaluations and math demonstrate significant memory gains for long sequences.
Weaknesses: The number of training examples for learning a single gabor filter seems large: 2^20 ~= 1 million. It would strengthen the result to see performance for a smaller set of training examples. The comparison to LSTMs could be made stronger by investigating multi-layer LSTMs with the same number of parameters. These may have better performance on the CLLM task. The authors refer to the model's ability to use long-term dependencies by pointing to performance on temporal-mnist and character level language modeling. In order to more specifically investigate the ability of these models to use long-term dependencies, I would suggest evaluation on permuted MNIST, as a comparison to temporal mnist where the pixels are in order. Additionally I would suggest evaluations on synthetic tasks meant to test ability to use long-term dependencies (e.g. a copying task).
Correctness: Proofs seem to be correct. Empirical evaluations span both simple function approximation as well as a temporal image classification task and language modeling. Empirical evaluations could be improved by evaluating on synthetic tasks and on permuted MNIST.
Clarity: Yes. Paper does a good job of explaining models, derivations, and experimental setup.
Relation to Prior Work: Mostly. Related work could be improved by citing and referring to work on truncated backpropagation through time (both heuristically set, and adaptively set - see example here: https://arxiv.org/pdf/1905.07473.pdf), as well as work on contractive recurrent backpropagation (see an example here: http://proceedings.mlr.press/v80/liao18c/liao18c.pdf) .
Additional Feedback: L41-42 should be rephrased. Lots of work has investigated efficient deployment of DNNs, either by compression, training binary networks, or working with lower-bit representations (e.g. float8). L75-76: I think you mean a memory cost of 1/l in comparison to LSTM? Figure 1: Can you explain if dashed connections in (c) have meaning? L139: Does using stochastic bit streams at inference time affect inference time? It would be great to see benchmarks of time taken for inference. L291-222: Do you have an intuition for why FSM epochs-to-convergence is affected less by longer inputs than for an LSTM? ----- I've read the author response. I appreciate the extra information and clarifications.