Instruct: Identify the main and secondary category of Arxiv papers based on the titles and abstracts
Query: An Application-Specific VLIW Processor with Vector Instruction Set for
  CNN Acceleration   In recent years, neural networks have surpassed classical algorithms in areas
such as object recognition, e.g. in the well-known ImageNet challenge. As a
result, great effort is being put into developing fast and efficient
accelerators, especially for Convolutional Neural Networks (CNNs). In this work
we present ConvAix, a fully C-programmable processor, which -- contrary to many
existing architectures -- does not rely on a hard-wired array of
multiply-and-accumulate (MAC) units. Instead it maps computations onto
independent vector lanes making use of a carefully designed vector instruction
set. The presented processor is targeted towards latency-sensitive applications
and is capable of executing up to 192 MAC operations per cycle. ConvAix
operates at a target clock frequency of 400 MHz in 28nm CMOS, thereby offering
state-of-the-art performance with proper flexibility within its target domain.
Simulation results for several 2D convolutional layers from well known CNNs
(AlexNet, VGG-16) show an average ALU utilization of 72.5% using vector
instructions with 16 bit fixed-point arithmetic. Compared to other well-known
designs which are less flexible, ConvAix offers competitive energy efficiency
of up to 497 GOP/s/W while even surpassing them in terms of area efficiency and
processing speed.

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The categories of this paper are: cmp-lg
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The categories of this paper are: alg-geom
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The categories of this paper are: quant-ph
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The categories of this paper are: acc-phys
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The categories of this paper are: solv-int
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The categories of this paper are: nucl-th
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The categories of this paper are: eess
0
The categories of this paper are: hep-ph
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The categories of this paper are: q-alg
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The categories of this paper are: hep-th
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The categories of this paper are: chem-ph
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The categories of this paper are: hep-ex
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The categories of this paper are: bayes-an
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The categories of this paper are: math-ph
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The categories of this paper are: physics
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The categories of this paper are: nlin