Neuromorphic computing offers excellent processing capabilities compared to classical general purpose hardware based on von Neumann architectures for AI models and algorithms. Existing architectures with separate processing and memory blocks are not ideal for energy-efficient learning and inference. For edge applications, it is envisioned that classical von Neumann computing will be replaced by innovative architectures, where memory and processing will be performed in the same locations, the so-called Computing In Memory (CIM). NeuSPIN project pursues the ambition vision of transferring AI algorithms traditionally performed in the cloud to complex on-chip bio-inspired AI hardware with high accuracy and extremely low energy consumption. The project objectives are to develop and deploy cross-disciplinary hardware and software allowing Green AI for the edge computing. It will consist in a combination of new flavors of Non-Volatile Memory Spintronic (NVM) technologies, with novel neurons and synapses designs leading to CIM neural network architectures. Non Volatile Spintronic technologies are a very promising approach for in-memory computing systems due to their efficient implementations. However, implementing edge-AI adapted algorithms remains a serious challenge due to multiple non ideal properties, in particular stochasticity and variabilities. New specific training algorithms such as Bayesian machine learning models adapted to both technology imperfections and neuromorphic designs will be developed and used in the spintronic-based neuromorphic hardware. Finally, the project objectives are not only to deliver cross cutting research on edge AI implementations, but also to create a strong European partnership between two top level, internationally recognized research centers and universities.