Massive adoption of computing in all aspects of human activity has led to unprecedented growth in the amount of data generated. Machine learning has been employed to classify and infer patterns from this abundance of raw data, at various levels of abstraction. Among the algorithms used, brain-inspired, or “neuromorphic”, computation provides a wide range of classification and/or prediction tools. Additionally, certain implementations come about with a significant promise of energy efficiency: highly optimized Deep Learning engines, ranging up to the efficiency promise of exploratory Spiking Neural Networks (SNN). Given the slowdown of silicon-only scaling, it is important to extend the roadmap of neuromorphic implementations by leveraging relevant technology innovations.
TEMPO will sweep technology options, covering emerging memories and 3D integration, and attempt to pair them with contemporary Deep Learning (DL) and exploratory (SNN) neuromorphic computing paradigms. The process and design compatibility of each technology option will be assessed with respect to established integration practices. Core computational kernels of such DL/SNN algorithms (e.g. dot-product or integrate-and-fire engines) will be brought into practice in representative demonstrators. To address the needs of end-users’ application sectors (aviation, automotive, etc.), this ECSEL JU project has integrated the main European actors of each sector to participate in the specification of requirements’ data-set definitions. This allows TEMPO partners to complement each other in a near-optimal way to provide Europe with a substantial competitive advantage and a faster time-to-market opportunity in the roll-out of neuromorphic implementations throughout the different sectors involved.