1. Quark: An Integer RISC-V Vector Processor for Sub-Byte Quantized DNN Inference
- Author
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AskariHemmat, MohammadHossein, Dupuis, Theo, Fournier, Yoan, Zarif, Nizar El, Cavalcante, Matheus, Perotti, Matteo, Gurkaynak, Frank, Benini, Luca, Leduc-Primeau, Francois, Savaria, Yvon, and David, Jean-Pierre
- Subjects
Computer Science - Hardware Architecture ,C.1.3 ,C.3 - 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., Comment: 5 pages. Accepted for publication in the 56th International Symposium on Circuits and Systems (ISCAS 2023)
- Published
- 2023