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A 28-nm 64-kb 31.6-TFLOPS/W Digital-Domain Floating-Point-Computing-Unit and Double-Bit 6T-SRAM Computing-in-Memory Macro for Floating-Point CNNs
A 28-nm 64-kb 31.6-TFLOPS/W Digital-Domain Floating-Point-Computing-Unit and Double-Bit 6T-SRAM Computing-in-Memory Macro for Floating-Point CNNs 150 150
Author(s): An Guo, Xi Chen, Fangyuan Dong, Xingyu Pu, Dongqi Li, Jingmin Zhang, Xueshan Dong, Hui Gao, Yiran Zhang, Bo Wang, Jun Yang, Xin Si

Abstract:

With the rapid advancement of artificial intelligence (AI), computing-in-memory (CIM) structure is proposed to improve energy efficiency (EF). However, previous CIMs often rely on INT8 data types, which pose challenges when addressing more complex networks, larger datasets, and increasingly intricate tasks. This work presents a double-bit 6T static random-access memory (…

Published in: IEEE Journal of Solid-State Circuits
Page(s): 1 – 13
Year of Publication: 2024
Electronic ISSN: 1558-173X
DOI: 10.1109/JSSC.2024.3375359
Publisher: IEEE

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EPU: An Energy-Efficient Explainable AI Accelerator With Sparsity-Free Computation and Heat Map Compression/Pruning
EPU: An Energy-Efficient Explainable AI Accelerator With Sparsity-Free Computation and Heat Map Compression/Pruning 150 150
Author(s): Junsoo Kim, Seunghee Han, Geonwoo Ko, Ji-Hoon Kim, Changha Lee, Taewoo Kim, Chan-Hyun Youn, Joo-Young Kim

Abstract:

Deep neural networks (DNNs) have recently gained significant prominence in various real-world applications such as image recognition, natural language processing, and autonomous vehicles. However, due to their black-box nature in system, the underlying mechanisms of DNNs behind the inference results remain opaque to users. In order to address this challenge, …

Published in: IEEE Journal of Solid-State Circuits
Page(s): 830 – 841
Year of Publication: 2024
Electronic ISSN: 1558-173X
DOI: 10.1109/JSSC.2023.3346913
Publisher: IEEE

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