BitBlade: Energy-Efficient Variable Bit-Precision Hardware Accelerator for Quantized Neural Networks-Reference-Cited by-同舟云学术

BitBlade: Energy-Efficient Variable Bit-Precision Hardware Accelerator for Quantized Neural Networks

Published:2022-06 Issue:6 Volume:57 Page:1924-1935
ISSN:0018-9200
Container-title:IEEE Journal of Solid-State Circuits
language:
Short-container-title:IEEE J. Solid-State Circuits

Author:

Ryu Sungju¹^ORCID,Kim Hyungjun²^ORCID,Yi Wooseok³,Kim Eunhwan²,Kim Yulhwa²^ORCID,Kim Taesu²^ORCID,Kim Jae-Joon⁴^ORCID

Affiliation:

1. School of Electronic Engineering, Soongsil University, Seoul, Republic of Korea

2. Department of Convergence IT Engineering, Pohang University of Science and Technology, Pohang, Republic of Korea

3. Department of Creative IT Engineering, Pohang University of Science and Technology (POSTECH), Pohang, Republic of Korea

4. Department of Electrical and Computer Engineering, Seoul National University, Seoul, Republic of Korea

Funder

Institute of Information & Communications Technology Planning & Evaluation (IITP) grant funded by the Korea Government [Ministry of Science and ICT (MSIT)]

National Research Foundation of Korea (NRF) grant funded by the Korea Government (MSIT)

Publisher

Institute of Electrical and Electronics Engineers (IEEE)

Subject

Electrical and Electronic Engineering

Link

http://xplorestaging.ieee.org/ielx7/4/9781662/09689050.pdf?arnumber=9689050

Reference21 articles.

1. UNPU: An Energy-Efficient Deep Neural Network Accelerator With Fully Variable Weight Bit Precision

2. Stripes: Bit-serial deep neural network computing

3. Eyeriss: An Energy-Efficient Reconfigurable Accelerator for Deep Convolutional Neural Networks

4. CMSIS-NN: Efficient neural network kernels for arm Cortex-M CPUs;lai;arXiv 1801 06601 [cs],2018

5. PULP-NN: accelerating quantized neural networks on parallel ultra-low-power RISC-V processors

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