Scalable Time-Domain Compute-in-Memory BNN Engine with 2.06 POPS/W Energy Efficiency for Edge-AI Devices-Reference-Cited by-同舟云学术

Scalable Time-Domain Compute-in-Memory BNN Engine with 2.06 POPS/W Energy Efficiency for Edge-AI Devices

Published:2023-06-05 Issue: Volume: Page:
ISSN:
Container-title:Proceedings of the Great Lakes Symposium on VLSI 2023
language:
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Author:

Lou Jie¹^ORCID,Freye Florian¹^ORCID,Lanius Christian¹^ORCID,Gemmeke Tobias¹^ORCID

Affiliation:

1. RWTH Aachen University, Aachen, Germany

Funder

Federal Ministry of Education and Research (BMBF, Germany) NEUROTEC II Project

Publisher

ACM

Link

https://dl.acm.org/doi/pdf/10.1145/3583781.3590220

Reference26 articles.

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3. Improving DNN Hardware Accuracy by In-Memory Computing Noise Injection

4. Yu-Der Chih et al. 2021. An 89TOPS/W and 16.3TOPS/mm2 All-Digital SRAMBased Full-Precision Compute-In Memory Macro in 22nm for Machine-Learning Edge Applications . In IEEE Int. Solid-State Circuits Conf. (ISSCC). 252--253 . https: //doi.org/10.1109/ISSCC42613. 2021 .9365766 10.1109/ISSCC42613.2021.9365766 Yu-Der Chih et al. 2021. An 89TOPS/W and 16.3TOPS/mm2 All-Digital SRAMBased Full-Precision Compute-In Memory Macro in 22nm for Machine-Learning Edge Applications. In IEEE Int. Solid-State Circuits Conf. (ISSCC). 252--253. https: //doi.org/10.1109/ISSCC42613.2021.9365766

5. An Energy-Efficient One-Shot Time-Based Neural Network Accelerator Employing Dynamic Threshold Error Correction in 65 nm

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