NEUROKIT2E

This European Project, comprised of 25 partners, aims to provide an open-source and sovereign platform of tools for Embedded AI with several ambitions:

1. Position EUROPE as a market leader by providing tools capable of meeting real-time, data confidentiality, energy consumption and usability requirements.
2. Provide a platform that will integrate hardware models with neural network models to optimize the network for embedded devices and provide a single end-to-end development chain.
3. Develop advanced compression and pruning methods to reduce model size while maintaining the performance of the original network.
4. Enable the utilization of synchronous coding (tensors) and event-driven coding (spikes) to be combined into the same network.

SAL's primary research focus in Neurokit2E is on optimizing hardware IP for AI applications, improving existing hardware operators, and developing specialized accelerators through high-level synthesis. This includes designing novel processing blocks for more compact neural network implementations, creating auxiliary units for efficient data management, and developing parametrizable neural network architectures to enhance performance and efficiency. Additionally, SAL is exploring AI optimization techniques such as quantization, pruning, and compression in the context of safety-critical applications.