The goal of the NEUROKIT2E project is to create an open-source Deep Learning framework for edge and embedded AI built around an established European value chain. This framework, called AIDGE, supports a wide range of application areas that operate independently and serve a global user community. It provides easy and fast full-stack solutions from Neural Network design and optimization to AI application development all the way down to hardware implementations while enabling code generation for application-specific targets. This platform provides flexibility for academic users in the AI domain to explore and innovate while allowing them the possibility to prototype systems, ensuring their work aligns well with industrial needs. This paper presents the results and achievements of the first part of this three-year project, along with its roadmap and expected outcomes.

In this paper, we present SENSIM, which is an open-source simulator designed specifically for the SENECA neuromorphic processor. This simulator is unique in that it combines features from both hardware-specific and hardware-agnostic spiking neural network simulators, resulting in a hybrid event-driven and time-step-driven simulation approach. This allows for flexibility between accuracy and speed during different stages of simulation. Our work highlights the open-source SENSIM platform, which enables the mapping of large-scale SNN/DNN models to the SENECA cores, as well as the benchmarking of crucial KPIs such as power and latency estimations.