  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.
