Recent breakthroughs in models of AI are correlated with the energy burden required to define and run these models. GPUs are the goto hardware for these implementations, since they can perform configurable, highly parallelised and matrix multiplications using digital circuits. To go beyond the energy limits of GPUs however, it may be required to abandon the digital computing paradigm altogether.

A particularly elegant solution may be to exploit the intrinsic physics of electron devices in an analogue fashion. For example, early work has already proposed how physical entropy of silicon devices can realise probabilistic learning algorithms, how voltage relaxation in resistive networks may approximate gradients, and how the activity of interconnected oscillators may converge minima on energy surfaces.

The objective of this thesis will be to study existing, and propose new, in-physics computing primitives. Furthermore, like GPUs bias current AI to rely on matrix multiplications, the candidate must also consider how these new primitives will impact future AI algorithms. Particular attention will be given to emerging nanodevice technologies under development at CEA Grenoble. Depending on the interests of the PhD student, it may be possible to design, tape-out and test circuit concepts leveraging these in-house innovative technologies.