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

arXiv 2401.06195 ↗First posted 11 Jan 2024cs.ET cs.AI cs.LGcited by 0

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Abstract (arXiv)

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.

Key facts. No citing papers in the map yet (data through 7 Oct 2026). Builds on 9 of its 24 references that are in the map. First posted on arXiv 11 Jan 2024.

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References · 9

  1. 2015Dropout as a Bayesian Approximation: Representing Model Uncertainty in Deep Learningcited by 4,416
  2. 2014Deep Neural Networks are Easily Fooled: High Confidence Predictions for Unrecognizable Imagescited by 774
  3. 2019DropConnect Is Effective in Modeling Uncertainty of Bayesian Deep Networkscited by 92
  4. 2020Chasing Carbon: The Elusive Environmental Footprint of Computingcited by 71
  5. 2018VIBNN: Hardware Acceleration of Bayesian Neural Networkscited by 19
  6. 2017Training Deep Convolutional Neural Networks with Resistive Cross-Point Devicescited by 17
  7. 2021Neural Sampling Machine with Stochastic Synapse allows Brain-like Learning and Inferencecited by 8
  8. 2023Spatial-SpinDrop: Spatial Dropout-based Binary Bayesian Neural Network with Spintronics Implementationcited by 7
  9. 2023Scale-Dropout: Estimating Uncertainty in Deep Neural Networks Using Stochastic Scalecited by 6

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