NeuSpin: Design of a Reliable Edge Neuromorphic System Based on Spintronics for Green AI

• URL: http://arxiv.org/abs/2401.06195v1
• Date: Thu, 11 Jan 2024 13:27:19 GMT
• Title: NeuSpin: Design of a Reliable Edge Neuromorphic System Based on Spintronics for Green AI
• Authors: Soyed Tuhin Ahmed, Kamal Danouchi, Guillaume Prenat, Lorena Anghel, Mehdi B. Tahoori
• Abstract summary: Internet of Things (IoT) and smart wearable devices for personalized healthcare will require storing and computing ever-increasing amounts of data. The key requirements for these devices are ultra-low-power, high-processing capabilities, autonomy at low cost, as well as reliability and accuracy to enable Green AI at the edge. The NeuSPIN project aims to address these challenges through full-stack hardware and software co-design, developing novel algorithmic and circuit design approaches to enhance the performance, energy-efficiency and robustness of BayNNs on sprint-based CIM platforms.
• Score: 0.22499166814992438
• License: http://creativecommons.org/licenses/by-nc-sa/4.0/
• Abstract: Internet of Things (IoT) and smart wearable devices for personalized healthcare will require storing and computing ever-increasing amounts of data. The key requirements for these devices are ultra-low-power, high-processing capabilities, autonomy at low cost, as well as reliability and accuracy to enable Green AI at the edge. Artificial Intelligence (AI) models, especially Bayesian Neural Networks (BayNNs) are resource-intensive and face challenges with traditional computing architectures due to the memory wall problem. Computing-in-Memory (CIM) with emerging resistive memories offers a solution by combining memory blocks and computing units for higher efficiency and lower power consumption. However, implementing BayNNs on CIM hardware, particularly with spintronic technologies, presents technical challenges due to variability and manufacturing defects. The NeuSPIN project aims to address these challenges through full-stack hardware and software co-design, developing novel algorithmic and circuit design approaches to enhance the performance, energy-efficiency and robustness of BayNNs on sprintronic-based CIM platforms.

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