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

arXiv: https://arxiv.org/abs/2401.06195 · HTML: https://mlsanity.com/paper/2401.06195/

## Key facts

- No citing papers in the map yet (data through 14 Oct 2026).
- Builds on 9 of its 24 references that are in the map.
- First posted on arXiv 11 Jan 2024.

## Builds on (most cited first, 9 of 9)

- [Dropout as a Bayesian Approximation: Representing Model Uncertainty in Deep Learning](https://mlsanity.com/paper/1506.02142/) (2015)
- [Deep Neural Networks are Easily Fooled: High Confidence Predictions for Unrecognizable Images](https://mlsanity.com/paper/1412.1897/) (2014)
- [DropConnect Is Effective in Modeling Uncertainty of Bayesian Deep Networks](https://mlsanity.com/paper/1906.04569/) (2019)
- [Chasing Carbon: The Elusive Environmental Footprint of Computing](https://mlsanity.com/paper/2011.02839/) (2020)
- [VIBNN: Hardware Acceleration of Bayesian Neural Networks](https://mlsanity.com/paper/1802.00822/) (2018)
- [Training Deep Convolutional Neural Networks with Resistive Cross-Point Devices](https://mlsanity.com/paper/1705.08014/) (2017)
- [Neural Sampling Machine with Stochastic Synapse allows Brain-like Learning and Inference](https://mlsanity.com/paper/2102.10477/) (2021)
- [Spatial-SpinDrop: Spatial Dropout-based Binary Bayesian Neural Network with Spintronics Implementation](https://mlsanity.com/paper/2306.10185/) (2023)
- [Scale-Dropout: Estimating Uncertainty in Deep Neural Networks Using Stochastic Scale](https://mlsanity.com/paper/2311.15816/) (2023)

## 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.