Accurate deep neural network inference using computational phase-change memory
oleh: Vinay Joshi, Manuel Le Gallo, Simon Haefeli, Irem Boybat, S. R. Nandakumar, Christophe Piveteau, Martino Dazzi, Bipin Rajendran, Abu Sebastian, Evangelos Eleftheriou
| Format: | Article |
|---|---|
| Diterbitkan: | Nature Portfolio 2020-05-01 |
Deskripsi
Designing deep learning inference hardware based on in-memory computing remains a challenge. Here, the authors propose a strategy to train ResNet-type convolutional neural networks which results in reduced accuracy loss when transferring weights to in-memory computing hardware based on phase-change memory.