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

Abstract password authentication.
PA password is required to view abstracts. You can find the password in the "Advance Program".The "Advance Program" is handed out at the registration desk to registered participants.

Presentation information

[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

Password