  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.
