Instruct: Identify the main and secondary category of Arxiv papers based on the titles and abstracts
Query: Accuracy vs. Efficiency: Achieving Both through FPGA-Implementation
  Aware Neural Architecture Search   A fundamental question lies in almost every application of deep neural
networks: what is the optimal neural architecture given a specific dataset?
Recently, several Neural Architecture Search (NAS) frameworks have been
developed that use reinforcement learning and evolutionary algorithm to search
for the solution. However, most of them take a long time to find the optimal
architecture due to the huge search space and the lengthy training process
needed to evaluate each candidate. In addition, most of them aim at accuracy
only and do not take into consideration the hardware that will be used to
implement the architecture. This will potentially lead to excessive latencies
beyond specifications, rendering the resulting architectures useless. To
address both issues, in this paper we use Field Programmable Gate Arrays
(FPGAs) as a vehicle to present a novel hardware-aware NAS framework, namely
FNAS, which will provide an optimal neural architecture with latency guaranteed
to meet the specification. In addition, with a performance abstraction model to
analyze the latency of neural architectures without training, our framework can
quickly prune architectures that do not satisfy the specification, leading to
higher efficiency. Experimental results on common data set such as ImageNet
show that in the cases where the state-of-the-art generates architectures with
latencies 7.81x longer than the specification, those from FNAS can meet the
specs with less than 1% accuracy loss. Moreover, FNAS also achieves up to
11.13x speedup for the search process. To the best of the authors' knowledge,
this is the very first hardware aware NAS.

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The categories of this paper are: hep-ph
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The categories of this paper are: hep-ex
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The categories of this paper are: q-fin
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The categories of this paper are: math-ph
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The categories of this paper are: bayes-an
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The categories of this paper are: funct-an
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The categories of this paper are: stat
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The categories of this paper are: gr-qc
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The categories of this paper are: cond-mat
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The categories of this paper are: math
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The categories of this paper are: plasm-ph
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The categories of this paper are: adap-org
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The categories of this paper are: comp-gas
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The categories of this paper are: physics
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The categories of this paper are: eess
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The categories of this paper are: ao-sci