Optimizing for In-Memory Deep Learning With Emerging Memory Technology

In-memory deep learning executes neural network models where they are stored, thus avoiding long-distance communication between memory and computation units, resulting in considerable savings in energy and time. In-memory deep learning has already demonstrated orders of magnitude higher performance...

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Bibliographic Details
Published in:IEEE transaction on neural networks and learning systems 2024-11, Vol.35 (11), p.15306-15320
Main Authors: Wang, Zhehui, Luo, Tao, Goh, Rick Siow Mong, Zhang, Wei, Wong, Weng-Fai
Format: Article
Language:English
Subjects:
Computational modeling
Deep learning
emerging memory technology (EMT)
Energy efficiency
Fluctuations
In-memory computing
Optimization
Phase change random access memory
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Summary:In-memory deep learning executes neural network models where they are stored, thus avoiding long-distance communication between memory and computation units, resulting in considerable savings in energy and time. In-memory deep learning has already demonstrated orders of magnitude higher performance density and energy efficiency. The use of emerging memory technology (EMT) promises to increase density, energy, and performance even further. However, EMT is intrinsically unstable, resulting in random data read fluctuations. This can translate to nonnegligible accuracy loss, potentially nullifying the gains. In this article, we propose three optimization techniques that can mathematically overcome the instability problem of EMT. They can improve the accuracy of the in-memory deep learning model while maximizing its energy efficiency. Experiments show that our solution can fully recover most models' state-of-the-art (SOTA) accuracy and achieves at least an order of magnitude higher energy efficiency than the SOTA.
ISSN:2162-237X
2162-2388
2162-2388
DOI:10.1109/TNNLS.2023.3285488