Instruct: Identify the main and secondary category of Arxiv papers based on the titles and abstracts
Query: Hardware-Software Codesign of Accurate, Multiplier-free Deep Neural
  Networks   While Deep Neural Networks (DNNs) push the state-of-the-art in many machine
learning applications, they often require millions of expensive floating-point
operations for each input classification. This computation overhead limits the
applicability of DNNs to low-power, embedded platforms and incurs high cost in
data centers. This motivates recent interests in designing low-power,
low-latency DNNs based on fixed-point, ternary, or even binary data precision.
While recent works in this area offer promising results, they often lead to
large accuracy drops when compared to the floating-point networks. We propose a
novel approach to map floating-point based DNNs to 8-bit dynamic fixed-point
networks with integer power-of-two weights with no change in network
architecture. Our dynamic fixed-point DNNs allow different radix points between
layers. During inference, power-of-two weights allow multiplications to be
replaced with arithmetic shifts, while the 8-bit fixed-point representation
simplifies both the buffer and adder design. In addition, we propose a hardware
accelerator design to achieve low-power, low-latency inference with
insignificant degradation in accuracy. Using our custom accelerator design with
the CIFAR-10 and ImageNet datasets, we show that our method achieves
significant power and energy savings while increasing the classification
accuracy.

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The categories of this paper are: nlin
12
The categories of this paper are: nucl-th
7
The categories of this paper are: hep-th
32
The categories of this paper are: atom-ph
23
The categories of this paper are: solv-int
6
The categories of this paper are: astro-ph
13
The categories of this paper are: quant-ph
22
The categories of this paper are: chao-dyn
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The categories of this paper are: dg-ga
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The categories of this paper are: hep-lat
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The categories of this paper are: adap-org
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The categories of this paper are: stat
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The categories of this paper are: cmp-lg
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The categories of this paper are: plasm-ph
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The categories of this paper are: q-alg
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The categories of this paper are: econ