mlsanityData to 14 Oct 2026

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

arXiv 2302.05996 ↗First posted 12 Feb 2023cs.ARcited by 2

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Abstract (arXiv)

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.

Key facts. Cited by 2 papers in the map (data through 14 Oct 2026). Builds on 13 of its 24 references that are in the map. First posted on arXiv 12 Feb 2023.

Builds on

13 of 24 references.

References · 13

  1. 2016XNOR-Net: ImageNet Classification Using Binary Convolutional Neural Networkscited by 849
  2. 2021A Survey of Quantization Methods for Efficient Neural Network Inferencecited by 815
  3. 2019Learned Step Size Quantizationcited by 628
  4. 2016FINN: A Framework for Fast, Scalable Binarized Neural Network Inferencecited by 150
  5. 2017Bit Fusion: Bit-Level Dynamically Composable Architecture for Accelerating Deep Neural Networkscited by 109
  6. 2021ReCU: Reviving the Dead Weights in Binary Neural Networkscited by 52
  7. 2016A near-threshold RISC-V core with DSP extensions for scalable IoT Endpoint Devicescited by 17
  8. 2019The Cost of Application-Class Processing: Energy and Performance Analysis of a Linux-ready 1.7GHz 64bit RISC-V Core in 22nm FDSOI Technologycited by 17
  9. 2020A Mixed-Precision RISC-V Processor for Extreme-Edge DNN Inferencecited by 13
  10. 2017Loom: Exploiting Weight and Activation Precisions to Accelerate Convolutional Neural Networkscited by 12
  11. 2022BARVINN: Arbitrary Precision DNN Accelerator Controlled by a RISC-V CPUcited by 6
  12. 2019Ara: A 1 GHz+ Scalable and Energy-Efficient RISC-V Vector Processor with Multi-Precision Floating Point Support in 22 nm FD-SOIcited by 5
1 more
  1. 2022A "New Ara" for Vector Computing: An Open Source Highly Efficient RISC-V V 1.0 Vector Processor Designcited by 5

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Cited by year

Citing papers per year: 2023: 1; 2024: 1.

2 papers.

  1. 2024SPEED: A Scalable RISC-V Vector Processor Enabling Efficient Multi-Precision DNN Inferencecited by 2
  2. 2023Sparq: A Custom RISC-V Vector Processor for Efficient Sub-Byte Quantized Inferencecited by 1

Similar, not cited

Close in wording, no citation either way

  1. 2021VS-Quant: Per-vector Scaled Quantization for Accurate Low-Precision Neural Network Inference
  2. 2025MaRVIn: A Cross-Layer Mixed-Precision RISC-V Framework for DNN Inference, from ISA Extension to Hardware Acceleration
  3. 2022FullPack: Full Vector Utilization for Sub-Byte Quantized Inference on General Purpose CPUs
  4. 2023Performance Characterization of using Quantization for DNN Inference on Edge Devices: Extended Version
  5. 2023A Precision-Scalable RISC-V DNN Processor with On-Device Learning Capability at the Extreme Edge
  6. 2023A 3 TOPS/W RISC-V Parallel Cluster for Inference of Fine-Grain Mixed-Precision Quantized Neural Networks
  7. 2026PoTAcc: A Pipeline for End-to-End Acceleration of Power-of-Two Quantized DNNs
  8. 2021Development of Quantized DNN Library for Exact Hardware Emulation