Sigma Delta Quantized Networks
Deep neural networks can be obscenely wasteful. When processing video, a convolutional network expends a fixed amount of computation for each frame with no regard to the similarity between neighbouring frames. As a result, it ends up repeatedly doing very similar computations. To put an end to such waste, we introduce Sigma-Delta networks. With each new input, each layer in this network sends a discretized form of its change in activation to the next layer. Thus the amount of computation that the network does scales with the amount of change in the input and layer activations, rather than the size of the network. We introduce an optimization method for converting any pre-trained deep network into an optimally efficient Sigma-Delta network, and show that our algorithm, if run on the appropriate hardware, could cut at least an order of magnitude from the computational cost of processing video data.
This paper presented a method of improving the efficiency of deep networks acting on a sequence of correlated inputs, by only performing the computations required to capture changes between adjacent inputs. The paper was clearly written, the approach is clever, and it*s neat to see a practical algorithm driven by what is essentially a spiking network. The benefits of this approach are still more theoretical than practical -- it seems unlikely to be worthwhile to do this on current hardware. I strongly suspect that if deep networks were trained with an appropriate sparse slowness penalty, the reduction in computation would be much larger.
You are a member of the scientific community tasked with peer review. Review the following paper title and abstract.
### Title + Abstract
Sigma Delta Quantized Networks
Deep neural networks can be obscenely wasteful. When processing video, a convolutional network expends a fixed amount of computation for each frame with no regard to the similarity between neighbouring frames. As a result, it ends up repeatedly doing very similar computations. To put an end to such waste, we introduce Sigma-Delta networks. With each new input, each layer in this network sends a discretized form of its change in activation to the next layer. Thus the amount of computation that the network does scales with the amount of change in the input and layer activations, rather than the size of the network. We introduce an optimization method for converting any pre-trained deep network into an optimally efficient Sigma-Delta network, and show that our algorithm, if run on the appropriate hardware, could cut at least an order of magnitude from the computational cost of processing video data.
user
<think>Okay, to do a decent peer review, I should start with summarizing what the work is about. This includes novelty, contributions, clarity, and impact. I might include suggestions to issues that I find critical to the usefulness of the work. Starting with novelty, there are different types of novelty. The first aspect of novelty is scope and scale. Is the work covering a broader perspective than previous works? The second aspect is methodology. Is the proposed methodology novel compared to previous methods? Do they provide any significant advantage over previous methodologies? The third aspect is results. Are the results different compared previous work? Does this lead to any novel theories or an extension of current theories? Next is clarity, I will be reading the work in depth and analyze if any sentences can be potentially ambiguous or misleading. If any sentences are ambiguous, I will state them and give a suggestion on how to rephrase such sentence or how to make more clear to the audience. Lastly, impact is important. I will attempt to predict how such work will impact the scientific community. This might include the applications of such work and how the work might transform the way we study a certain topic. Finally, I will might only mention issues that are critical. This is to keep the review as concise and helpful as possible.</think>



This paper presented a method of improving the efficiency of deep networks acting on a sequence of correlated inputs, by only performing the computations required to capture changes between adjacent inputs. The paper was clearly written, the approach is clever, and it*s neat to see a practical algorithm driven by what is essentially a spiking network. The benefits of this approach are still more theoretical than practical -- it seems unlikely to be worthwhile to do this on current hardware. I strongly suspect that if deep networks were trained with an appropriate sparse slowness penalty, the reduction in computation would be much larger.
assistant
This paper presented a method of improving the efficiency of deep networks acting on a sequence of correlated inputs, by only performing the computations required to capture changes between adjacent inputs. The paper was clearly written, the approach is clever, and it*s neat to see a practical algorithm driven by what is essentially a spiking network. The benefits of this approach are still more theoretical than practical -- it seems unlikely to be worthwhile to do this on current hardware. I strongly suspect that if deep networks were trained with an appropriate sparse slowness penalty, the reduction in computation would be much larger.