Training

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A Dynamic Execution Neural Network Processor for Fine-Grained Mixed-Precision Model Training Based on Online Quantization Sensitivity Analysis
A Dynamic Execution Neural Network Processor for Fine-Grained Mixed-Precision Model Training Based on Online Quantization Sensitivity Analysis 150 150
Author(s): Ruoyang Liu, Chenhan Wei, Yixiong Yang, Wenxun Wang, Binbin Yuan, Huazhong Yang, Yongpan Liu

Abstract:

BSTcontrol As neural network (NN) training cost red has been growing exponentially over the past decade, developing high-speed and energy-efficient training methods has become an urgent task. Fine-grained mixed-precision low-bit training is the most promising way for high-efficiency training, but it needs dedicated processor designs to overcome the overhead in …

Published in: IEEE Journal of Solid-State Circuits
Page(s): 1 – 12
Year of Publication: 2024
Electronic ISSN: 1558-173X
DOI: 10.1109/JSSC.2024.3377292
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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