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

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

The analog AI core concept is appealing for deep‐learning (DL) because it combines computation and memory functions into a single device. Yet, significant challenges such as noise and weight drift will impact large‐scale analog in‐memory computing. Here, effects of flicker noise and drift on large D...

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Bibliographic Details
Main Authors: Jin-Ping Han, Malte J. Rasch, Zuoguang Liu, Paul Solomon, Kevin Brew, Kangguo Cheng, Injo Ok, Victor Chan, Michael Longstreet, Wanki Kim, Robert L. Bruce, Cheng-Wei Cheng, Nicole Saulnier, Vijay Narayanan
Format: Article
Language:English
Published: Wiley 2022-05-01
Series:Advanced Intelligent Systems
Subjects:
analog RPU inference
deep-learning networks
flicker noise
hardware awareness training
PCM
weight drift
Online Access:https://doi.org/10.1002/aisy.202100179

Internet

https://doi.org/10.1002/aisy.202100179

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