Quark: An Integer RISC-V Vector Processor for Sub-Byte Quantized DNN Inference
MohammadHossein AskariHemmat, Théo Dupuis, Yoan Fournier, Nizar El Zarif, Matheus Cavalcante, Matteo Perotti, Frank K. Gürkaynak, Luca Benini, François Leduc-Primeau, Yvon Savaria, Jean‐Pierre David
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.