GQNA: Generic Quantized DNN Accelerator With Weight-Repetition-Aware Activation Aggregating-Reference-Cited by-同舟云学术

GQNA: Generic Quantized DNN Accelerator With Weight-Repetition-Aware Activation Aggregating

Published:2022-10 Issue:10 Volume:69 Page:4069-4082
ISSN:1549-8328
Container-title:IEEE Transactions on Circuits and Systems I: Regular Papers
language:
Short-container-title:IEEE Trans. Circuits Syst. I

Author:

Yang Jianxun¹^ORCID,Tu Fengbin¹,Li Yixuan¹,Wang Yiqi¹,Liu Leibo¹^ORCID,Wei Shaojun¹^ORCID,Yin Shouyi¹^ORCID

Affiliation:

1. School of Integrated Circuits, Beijing National Research Center for Information Science and Technology, and Beijing Advanced Innovation Center, Tsinghua University, Beijing, China

Funder

NSFC

National Key Research and Development Program

Beijing Science and Technology Project

Beijing Advanced Innovation Center

Publisher

Institute of Electrical and Electronics Engineers (IEEE)

Subject

Electrical and Electronic Engineering,Hardware and Architecture

Link

http://xplorestaging.ieee.org/ielx7/8919/9904939/09829865.pdf?arnumber=9829865

Reference38 articles.

1. BitSystolic: A 26.7 TOPS/W 2b~8b NPU With Configurable Data Flows for Edge Devices

2. YodaNN: An Architecture for Ultralow Power Binary-Weight CNN Acceleration

3. An Energy-Efficient Architecture for Binary Weight Convolutional Neural Networks

4. A 44.1TOPS/W Precision-Scalable Accelerator for Quantized Neural Networks in 28nm CMOS

5. 9.2 A 28 nm 12.1 TOPS/W dual-mode CNN processor using effective-weight-based convolution and error-compensation-based prediction;mo;IEEE Int Solid-State Circuits Conf (ISSCC) Dig Tech Papers,2021

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