Instruct: Identify the main and secondary category of Arxiv papers based on the titles and abstracts
Query: You Only Spike Once: Improving Energy-Efficient Neuromorphic Inference
  to ANN-Level Accuracy   In the past decade, advances in Artificial Neural Networks (ANNs) have
allowed them to perform extremely well for a wide range of tasks. In fact, they
have reached human parity when performing image recognition, for example.
Unfortunately, the accuracy of these ANNs comes at the expense of a large
number of cache and/or memory accesses and compute operations. Spiking Neural
Networks (SNNs), a type of neuromorphic, or brain-inspired network, have
recently gained significant interest as power-efficient alternatives to ANNs,
because they are sparse, accessing very few weights, and typically only use
addition operations instead of the more power-intensive multiply-and-accumulate
(MAC) operations. The vast majority of neuromorphic hardware designs support
rate-encoded SNNs, where the information is encoded in spike rates.
Rate-encoded SNNs could be seen as inefficient as an encoding scheme because it
involves the transmission of a large number of spikes. A more efficient
encoding scheme, Time-To-First-Spike (TTFS) encoding, encodes information in
the relative time of arrival of spikes. While TTFS-encoded SNNs are more
efficient than rate-encoded SNNs, they have, up to now, performed poorly in
terms of accuracy compared to previous methods. Hence, in this work, we aim to
overcome the limitations of TTFS-encoded neuromorphic systems. To accomplish
this, we propose: (1) a novel optimization algorithm for TTFS-encoded SNNs
converted from ANNs and (2) a novel hardware accelerator for TTFS-encoded SNNs,
with a scalable and low-power design. Overall, our work in TTFS encoding and
training improves the accuracy of SNNs to achieve state-of-the-art results on
MNIST MLPs, while reducing power consumption by 1.46$\times$ over the
state-of-the-art neuromorphic hardware.

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The categories of this paper are: supr-con
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The categories of this paper are: astro-ph
7
The categories of this paper are: hep-th
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The categories of this paper are: quant-ph
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The categories of this paper are: ao-sci
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The categories of this paper are: adap-org
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The categories of this paper are: mtrl-th
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The categories of this paper are: econ
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The categories of this paper are: hep-ex
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The categories of this paper are: math
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The categories of this paper are: patt-sol
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The categories of this paper are: hep-lat
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The categories of this paper are: physics
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The categories of this paper are: alg-geom
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The categories of this paper are: nlin
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The categories of this paper are: math-ph