CENTRALE LYON - Post-doctoral researcher (12 months) Mathematics of energy-efficient AI: diffusion models on emerging hardware

The question Generating one image with a diffusion model means evaluating a large neural network tens to hundreds of times. That repetition is where the energy goes, and it is part of why data-centre demand is now visible at grid scale: the Lawrence Berkeley National Laboratory puts US data-centre electricity use at 176 TWh in 2023, 4.4 % of national consumption, a figure that more than doubled since 2017 largely because of AI servers, and projects 325–580 TWh by 2028. Emerging hardware attacks exactly this primitive: analog in-memory computing, silicon photonics, ferroelectric devices and stochastic computing perform the underlying matrix–vector products at lower energy than digital CMOS, under conditions on scale and precision, but return a perturbed result. The usual objection is that nobody can say in advance how much perturbation a model tolerates. In EMMA, this opportunity is pursued through a PCM-based photonic matrix–vector engine as the primary demonstrator, complemented where appropriate by FeFET-based analog computing, so that device-level non-idealities are treated as explicit design parameters rather than as after-the-fact implementation losses. Diffusion models are an unusual case in which that question has a mathematical answer: their convergence theory bounds the discrepancy between the generated and target distributions by three terms, of which only one, the L 2 error of the learned score, depends on the hardware. That side is now in good shape: for stochasti