Low-Power and Scalable BEOL-Compatible IGZO TFT eDRAM-Based Charge-Domain Computing-Reference-Cited by-同舟云学术

Low-Power and Scalable BEOL-Compatible IGZO TFT eDRAM-Based Charge-Domain Computing

Published:2023-12 Issue:12 Volume:70 Page:5166-5179
ISSN:1549-8328
Container-title:IEEE Transactions on Circuits and Systems I: Regular Papers
language:
Short-container-title:IEEE Trans. Circuits Syst. I

Author:

Tang Wenjun¹^ORCID,Liu Jialong¹^ORCID,Sun Chen²^ORCID,Zheng Zijie²^ORCID,Liu Yongpan¹^ORCID,Yang Huazhong¹^ORCID,Jiang Chen¹^ORCID,Ni Kai³^ORCID,Gong Xiao²^ORCID,Li Xueqing¹^ORCID

Affiliation:

1. Department of Electronic Engineering, BNRist, Tsinghua University, Beijing, China

2. Department of Electrical and Computer Engineering, National University of Singapore, Cluny Road, Singapore

3. Department of Electrical Engineering, University of Notre Dame, Notre Dame, IN, USA

Funder

National Key Research and Development Program of China

NSFC

Advanced Research and Technology Innovation Centre (ARTIC) Program, National University of Singapore

Publisher

Institute of Electrical and Electronics Engineers (IEEE)

Subject

Electrical and Electronic Engineering,Hardware and Architecture

Link

http://xplorestaging.ieee.org/ielx7/8919/10363413/10268258.pdf?arnumber=10268258

Reference50 articles.

1. 14.6 A 0.62mW ultra-low-power convolutional-neural-network face-recognition processor and a CIS integrated with always-on haar-like face detector

2. A 5.1pJ/Neuron 127.3us/Inference RNN-based Speech Recognition Processor using 16 Computing-in-Memory SRAM Macros in 65nm CMOS

3. A 55nm time-domain mixed-signal neuromorphic accelerator with stochastic synapses and embedded reinforcement learning for autonomous micro-robots

4. Can programming be liberated from the von Neumann style?

5. Challenges and Trends of SRAM-Based Computing-In-Memory for AI Edge Devices

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