Impact of Phase‐Change Memory Flicker Noise and Weight Drift on Analog Hardware Inference for Large‐Scale Deep Learning Networks

Author:

Han Jin-Ping1ORCID,Rasch Malte J.1,Liu Zuoguang2,Solomon Paul1,Brew Kevin2,Cheng Kangguo2,Ok Injo2,Chan Victor2,Longstreet Michael3,Kim Wanki1,Bruce Robert L.1,Cheng Cheng-Wei1,Saulnier Nicole2,Narayanan Vijay1

Affiliation:

1. IBM T. J. Watson Research Center 1101 Kitchawan Road Yorktown Heights NY 10598 USA

2. IBM Research - Albany Nano Tech 257 Fuller Road Albany NY 12203 USA

3. IBM System 2070 Route 52 Hopewell Junction NY 12533 USA

Publisher

Wiley

Reference33 articles.

1. Deep learning

2. C.‐Y.Chen J.Choi K.Gopalakrishnan V.Srinivasan S.Venkataramani inDesign Automation & Test in Europe Conf. and Exhibition (DATE) IEEE Piscataway NJ2018 pp.821–826.

3. S.Venkataramani J.Choi V.Srinivasan K.Gopalakrishnan L.Chang inIEEE Int. Symp. on Workload Characterization (IISWC) IEEE Piscataway NJ2019 pp.257–262.

4. A role for analogue memory in AI hardware

5. Equivalent-accuracy accelerated neural-network training using analogue memory

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