7 papers
CN101 - A Digital Thermodynamic Computer for Generative AI
Lars Holdijk, Denis Melanson, Zier Mensch +14
Thermodynamic computing is an emerging hardware paradigm, in which stochastic physical dynamics serve as the direct computational primitive. The recent explosion of generative AI h…
Robust Stochastic Gradient Posterior Sampling with Lattice Based Discretisation
Zier Mensch, Lars Holdijk, Samuel Duffield +4
Stochastic-gradient MCMC methods enable scalable Bayesian posterior sampling but often suffer from sensitivity to minibatch size and gradient noise. To address this, we propose Sto…
Lattice Random Walk Discretisations of Stochastic Differential Equations
Samuel Duffield, Maxwell Aifer, Denis Melanson +2
We introduce a lattice random walk discretisation scheme for stochastic differential equations (SDEs) that samples binary or ternary increments at each step, suppressing complex dr…
Energy-Time-Accuracy Tradeoffs in Thermodynamic Computing
Alberto Rolandi, Paolo Abiuso, Patryk Lipka-Bartosik +3
In the paradigm of thermodynamic computing, instead of behaving deterministically, hardware undergoes a stochastic process in order to sample from a distribution of interest. While…
Solving the compute crisis with physics-based ASICs
Maxwell Aifer, Zach Belateche, Suraj Bramhavar +11
Escalating artificial intelligence (AI) demands expose a critical "compute crisis" characterized by unsustainable energy consumption, prohibitive training costs, and the approachin…
Scalable Thermodynamic Second-order Optimization
Kaelan Donatella, Samuel Duffield, Denis Melanson +7
Many hardware proposals have aimed to accelerate inference in AI workloads. Less attention has been paid to hardware acceleration of training, despite the enormous societal impact…