Analysis of Inference Accuracy of Convolutional Neural Networks due to Quantization of Weights, Gradients, Input and Output Signals Stemming from Computation-in-Memory

Adil Padiyal

講演情報

Short Oral Presentation

02: Advanced and Emerging Memories / New Applications

[SO-PS-02] 02: Advanced and Emerging Memories / New Applications

2023年9月7日(木) 13:30 〜 14:18 Room K (234, Bldg. 2)

Session Chairs: Atsushi Himeno (Panasonic Holdings Corporation), Ming-Hsiu Lee (Macronix International Co., Ltd)

13:30 〜 13:32

[SO-PS-02-01 (Late News)] Analysis of Inference Accuracy of Convolutional Neural Networks due to Quantization of Weights, Gradients, Input and Output Signals Stemming from Computation-in-Memory

^○Adil Padiyal¹, Ayumu Yamada¹, Naoko Misawa¹, Chihiro Matsui¹, Ken Takeuchi¹ (1.The University of Tokyo (Japan))

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講演情報

[SO-PS-02] 02: Advanced and Emerging Memories / New Applications

[SO-PS-02-01 (Late News)] Analysis of Inference Accuracy of Convolutional Neural Networks due to Quantization of Weights, Gradients, Input and Output Signals Stemming from Computation-in-Memory

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