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arXiv · 1904.05106

An Application-Specific VLIW Processor with Vector Instruction Set for CNN Acceleration

Andreas Bytyn·Rainer Leupers·Gerd Ascheid
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Abstract

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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Andreas Bytyn, Rainer Leupers, Gerd Ascheid. 2019-04-10. An Application-Specific VLIW Processor with Vector Instruction Set for CNN Acceleration. https://doi.org/10.1109/iscas.2019.8702357

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