Using the IBM analog in-memory hardware acceleration kit for neural network training and inference
Analog In-Memory Computing (AIMC) is a promising approach to reduce the latency and energy consumption of Deep Neural Network (DNN) inference and training. However, the noisy and non-linear device characteristics and the non-ideal peripheral circuitry in AIMC chips require adapting DNNs to be deploy...
| Main Authors: | , , , , , , , , , , |
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| Format: | Article |
| Language: | English |
| Published: |
AIP Publishing LLC
2023-12-01
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| Series: | APL Machine Learning |
| Online Access: | http://dx.doi.org/10.1063/5.0168089 |