Quark: An Integer RISC-V Vector Processor for Sub-Byte Quantized DNN Inference

MohammadHossein AskariHemmat; Theo Dupuis; Yoan Fournier; Nizar El Zarif; Matheus Cavalcante; Matteo Perotti; Frank Gurkaynak; Luca Benini; Francois Leduc-Primeau; Yvon Savaria; Jean-Pierre David

arXiv · 2302.05996

Quark: An Integer RISC-V Vector Processor for Sub-Byte Quantized DNN Inference

MohammadHossein AskariHemmat·Theo Dupuis·Yoan Fournier·Nizar El Zarif·Matheus Cavalcante·Matteo Perotti·Frank Gurkaynak·Luca Benini·Francois Leduc-Primeau·Yvon Savaria·Jean-Pierre David

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Abstract

In this paper, we present Quark, an integer RISC-V vector processor specifically tailored for sub-byte DNN inference. Quark is implemented in GlobalFoundries' 22FDX FD-SOI technology. It is designed on top of Ara, an open-source 64-bit RISC-V vector processor. To accommodate sub-byte DNN inference, Quark extends Ara by adding specialized vector instructions to perform sub-byte quantized operations. We also remove the floating-point unit from Quarks' lanes and use the CVA6 RISC-V scalar core for the re-scaling operations that are required in quantized neural network inference. This makes each lane of Quark 2 times smaller and 1.9 times more power efficient compared to the ones of Ara. In this paper we show that Quark can run quantized models at sub-byte precision. Notably we show that for 1-bit and 2-bit quantized models, Quark can accelerate computation of Conv2d over various ranges of inputs and kernel sizes.

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MohammadHossein AskariHemmat, Theo Dupuis, Yoan Fournier, Nizar El Zarif, Matheus Cavalcante, Matteo Perotti, Frank Gurkaynak, Luca Benini, Francois Leduc-Primeau, Yvon Savaria, Jean-Pierre David. 2023-02-12. Quark: An Integer RISC-V Vector Processor for Sub-Byte Quantized DNN Inference. https://arxiv.org/abs/2302.05996

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