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

doi:https://doi.org/10.7567/SSDM.2023.PS-2-01

[PS-2-01 (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))

https://doi.org/10.7567/SSDM.2023.PS-2-01

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講演情報

[PS-2] 02: Advanced and Emerging Memories / New Applications

[PS-2-01 (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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