An Energy-Efficient Reconfigurable AI-Based Object Detection and Tracking Processor Supporting Online Object Learning-Reference-Cited by-同舟云学术

An Energy-Efficient Reconfigurable AI-Based Object Detection and Tracking Processor Supporting Online Object Learning

Published:2022 Issue: Volume:5 Page:78-81
ISSN:2573-9603
Container-title:IEEE Solid-State Circuits Letters
language:
Short-container-title:IEEE Solid-State Circuits Lett.

Author:

Gong Yuchuan¹^ORCID,Zhang Teng¹,Guo Hongtao¹^ORCID,Liu Qingsong¹^ORCID,Que Luying¹,Jia Conghan¹,Huang Jiahui¹,Liu Ye¹,Gan Jiayan¹,Xie Yuxiang¹,Zhou Yong¹,Liu Lili¹,Xiang Xiaoqiang¹,Chang Liang¹^ORCID,Yan Rui²,Zhou Jun¹^ORCID

Affiliation:

1. School of Information and Communication Engineering, University of Electronic Science and Technology of China, Chengdu, China

2. College of Computer Science, Zhejiang University of Technology, Hangzhou, China

Funder

National Safety Academic Fund

Publisher

Institute of Electrical and Electronics Engineers (IEEE)

Subject

Electrical and Electronic Engineering

Link

http://xplorestaging.ieee.org/ielx7/8011414/9677053/09745082.pdf?arnumber=9745082

Reference10 articles.

1. MN-Core - A Highly Efficient and Scalable Approach to Deep Learning

2. 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

3. A 1.15-TOPS 6.57-TOPS/W Neural Network Processor for Multi-Scale Object Detection With Reduced Convolutional Operations

4. Going Deeper with Embedded FPGA Platform for Convolutional Neural Network

5. A 68-mw 2.2 Tops/w Low Bit Width and Multiplierless DCNN Object Detection Processor for Visually Impaired People

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