Paper:
Event-based neuromorphic systems promise to reduce the energy consumption of deep neural networks by replacing expensive floating point operations on dense matrices by low energy, sparse operations on spike events. While these systems can be trained increasingly well using approximations of the backpropagation algorithm, this usually requires high precision errors and is therefore incompatible with the typical communication infrastructure of neuromorphic circuits. In this work, we analyze how the gradient can be discretized into spike events when training a spiking neural network. To accelerate our simulation, we show that using a special implementation of the integrate-and-fire neuron allows us to describe the accumulated activations and errors of the spiking neural network in terms of an equivalent artificial neural network, allowing us to largely speed up training compared to an explicit simulation of all spike events. This way we are able to demonstrate that even for deep networks, the gradients can be discretized sufficiently well with spikes if the gradient is properly rescaled. This form of spike-based backpropagation enables us to achieve equivalent or better accuracies on the MNIST and CIFAR10 datasets than comparable state-of-the-art spiking neural networks trained with full precision gradients. The algorithm, which we call SpikeGrad, is based on accumulation and comparison operations and can naturally exploit sparsity in the gradient computation, which makes it an interesting choice for a spiking neuromorphic systems with on-chip learning capacities.

Review:
EDIT After Rebuttal: My understanding of the contributions of this paper has improved. I now increase my score to a weak accept.
This paper proposes a new backpropagation algorithm learning algorithm "SpikeGrad" for Spike-based neural network paradigm. Simulating this algorithm on a classical hardware would require a lot of time-steps. To circumvent this, they show how to construct a corresponding artificial neural net (that can be trained using the traditional gradient based algorithms) which is equivalent to the spiking neural net. Using this equivalence they simulate a large scale SNN on many real-world dataset (first paper to do so). In particular, they use MNIST and CIFAR-10 for this purpose. They show that training a fixed architecture using their method is comparable to other prior work which uses high-precision gradients to train them. They also show how to exploit sparsity of the gradient in the back propagation for SNN.
This paper is hard-to-follow for someone not familiar with the background material. In particular, without looking at prior literature it was hard to understand that "integrate and fire neuron model" is essentially the feedforward mechanism for the SNN. I would suggest the authors make this a bit more explicit. Moreover, it would serve the structuring of the paper to have a formal "Preliminaries" section, where all known stuff goes. It was hard to discern what is new in this paper, and what is from prior work and these are mixed in section 2. For instance, section 2 states "SpikeGrad" algorithm; but the main contribution (ie., the back propagation algorithm) only appears in the middle of this section. Likewise, I think section 3 can be arranged better. In particular, the equivalence is a "formal" statement and thus, could be stated as a theorem followed by a proof. It will also make it explicit as to what does it mean by an "equivalent" network. In fact, it is still not clear to me at this point what that statement means. Could you please elaborate this in the rebuttal? 
Regarding the conceptual contribution of this paper, if I understood things correctly, the main claim is that they give a new way to train SNN whose performance on MNIST and CIFAR-10 is comparable to other works. The second contribution is that they give the equivalence between ANN and SNN (point above). It is also unclear to me what the point regarding the sparse gradient in the backpropagation in the experimental section is trying to make? Could you please clarify this in the rebuttal as well?
At this point, the writing of this paper leaves me with many unanswered questions that needs to be addressed before I can make a more informed decision. Please provide those in the rebuttal and based on those will update my final score. But with my current understanding of this paper, I think this does not meet the bar. The contributions in this paper do not seem too significant.