The objective of our collaborative multi-partner project is to create an open-source Deep Learning framework called AIDGE for edge and embedded Artificial Intelligence (AI), built around an established European value chain. The framework is designed to support diverse application domains that function independently while serving a broad international community. It offers an integrated, full-stack workflow from Neural Network design and optimization to AI application development and hardware-level implementation with automated code generation for specific hardware targets. The platform aims to provide researchers and developers with a flexible environment to explore novel AI concepts, rapidly prototype solutions, and ensure strong alignment between academic research and industrial requirements. This paper summarizes the progress, outcomes, and milestones achieved up to the second year of this three-year project.

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.

Neuronflow is a neuromorphic, many core, data flow architecture that exploits brain-inspired concepts to deliver a scalable event-based processing engine for neuron networks in Live AI applications. Its design is inspired by brain biology, but not necessarily biologically plausible. The main design goal is the exploitation of sparsity to dramatically reduce latency and power consumption as required by sensor processing at the Edge.

There is wide consensus that the prefrontal cortex (PFC) is able to exert cognitive control on behavior by biasing processing toward task-relevant information and by modulating response selection. This idea is typically framed in terms of top-down influences within a cortical control hierarchy, where prefrontal-basal ganglia loops gate multiple input-output channels, which in turn can activate or sequence motor primitives expressed in (pre-)motor cortices. Here we advance a new hypothesis, based on the notion of programmability and an interpreter-programmer computational scheme, on how the PFC can flexibly bias the selection of sensorimotor patterns depending on internal goal and task contexts. In this approach, multiple elementary behaviors representing motor primitives are expressed by a single multi-purpose neural network, which is seen as a reusable area of "recycled" neurons (interpreter). The PFC thus acts as a "programmer" that, without modifying the network connectivity, feeds the interpreter networks with specific input parameters encoding the programs (corresponding to network structures) to be interpreted by the (pre-)motor areas. Our architecture is validated in a standard test for executive function: the 1-2-AX task. Our results show that this computational framework provides a robust, scalable and flexible scheme that can be iterated at different hierarchical layers, supporting the realization of multiple goals. We discuss the plausibility of the "programmer-interpreter" scheme to explain the functioning of prefrontal-(pre)motor cortical hierarchies.