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 stochastic samplers, bounds linear in the dimension under minimal assumptions on the data; for deterministic samplers, a matching theory under regularity.
What is missing is the bridge.
The theory is parameterised by a score error, the hardware literature by conductance noise, effective number of bits and photons per multiply–accumulate; nobody has written the map between them, in either direction.
The quantisation literature for diffusion models reports image-quality scores; the induced L 2 score err.