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.

翻译：摘要：用于个性化医疗的物联网（IoT）和智能可穿戴设备需要存储和处理日益增长的数据量。这些设备的关键要求包括超低功耗、高处理能力、低成本自主性，以及可靠性和准确性，以实现边缘端的绿色人工智能（Green AI）。人工智能（AI）模型，特别是贝叶斯神经网络（BayNNs），资源密集且因内存墙问题面临传统计算架构的挑战。基于新兴阻变存储器的存内计算（CIM）通过融合存储模块与计算单元提供解决方案，以提高效率并降低功耗。然而，在CIM硬件上实现BayNNs，尤其采用自旋电子学技术时，由于器件变异性与制造缺陷带来了技术挑战。NeuSPIN项目旨在通过全栈硬件与软件协同设计应对这些挑战，开发新型算法与电路设计方案，以增强基于自旋电子学CIM平台上BayNNs的性能、能效与鲁棒性。