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...

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Bibliografiske detaljer

Principais autores:	Manuel Le Gallo, Corey Lammie, Julian Büchel, Fabio Carta, Omobayode Fagbohungbe, Charles Mackin, Hsinyu Tsai, Vijay Narayanan, Abu Sebastian, Kaoutar El Maghraoui, Malte J. Rasch
Format:	Artigo
Sprog:	Inglês
Udgivet:	AIP Publishing LLC 2023-12-01
Serier:	APL Machine Learning
Online adgang:	http://dx.doi.org/10.1063/5.0168089
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