An EU Project that targets the development of low-power chips for AI applications

TEMPO is a first step in the direction of creating an implementation plan that supports the creation of a pan-European research infrastructure for advanced computing technologies. A major goal of the project is to bring together the world class expertise and infrastructures of Imec, LETI and Fraunhofer-Verbunds Mikroelektronik, and together with semiconductor companies and system houses to explore the possibilities of the developed technology. In the project, seven use-cases will be assessed in key domains where Europe is strong (automotive, space and health). The aim is to re-inforce and keep strong leadership in these areas by already bringing industry in contact with future technologies at low TRL level.

Neuromorphic Engineering

A Ground-breaking approach inspired on biological neural processing systems

Neuromorphic engineering is a ground-breaking approach to the design of computing technology that draws inspiration from the powerful and efficient biological neural processing systems. Neuromorphic devices are able to carry out sensing, processing, and motor control strategies with ultra-low power performance. Today's neuromorphic community in Europe is leading the State-of-the-Art in this domain. The community counts an increasing number of labs that work on theory, modelling, and implementation of neuromorphic computing systems using both conventional VLSI technologies, emerging memristive devices, photonics, spin-based, and other nano-technological solutions. To enable the uptake of this technology and to match the needs of real-world applications in future products that solve real-world tasks in industry, health-care, assistive systems, and consumer devices, extensive work is needed in terms of neuromorphic algorithms, emerging technologies, hardware design and neuromorphic applications respectively. It is important to note that by “neuromorphic”, the TEMPO project perceives brain-inspired algorithms and, given the community’s emphasis, focuses specifically on conventional Deep Learning (DL) and Spiking Neural Networks (SNNs). That way, it is ensured that both established paradigms are covered in the greater domain of brain-inspired computation.