The Effect of Asymmetric Transistor Aging on Systolic Arrays for Mission Critical Machine Learning Applications

The Effect of Asymmetric Transistor Aging on Systolic Arrays for Mission Critical Machine Learning Applications

Deep neural networks (DNNs) excel in various applications, such as computer vision, natural language processing, and other mission-critical systems. As the computational complexity of these models grows, there is an increasing need for specialized accelerators to handle the demanding workloads. In r...

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Bibliographic Details
Main Authors: Firas Ramadan, Gil Shomron, Freddy Gabbay
Format: Article
Language:English
Published: IEEE 2025-01-01
Series:IEEE Access
Subjects:
Asymmetric aging
bias temperature instability
deep neural networks
machine learning accelerators
mission critical applications
systolic arrays
Online Access:https://ieeexplore.ieee.org/document/10915624/
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