4 citations · 14 across the 12 of their papers we have counts for
13 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…
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…
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…
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…
Thermodynamic Algorithms for Quadratic Programming
Patryk-Lipka Bartosik, Kaelan Donatella, Maxwell Aifer +4
Thermodynamic computing has emerged as a promising paradigm for accelerating computation by harnessing the thermalization properties of physical systems. This work introduces a nov…
Thermodynamic Bayesian Inference
Maxwell Aifer, Samuel Duffield, Kaelan Donatella +6
A fully Bayesian treatment of complicated predictive models (such as deep neural networks) would enable rigorous uncertainty quantification and the automation of higher-level tasks…
Thermodynamic Natural Gradient Descent
Kaelan Donatella, Samuel Duffield, Maxwell Aifer +3
Second-order training methods have better convergence properties than gradient descent but are rarely used in practice for large-scale training due to their computational overhead.…
Error Mitigation for Thermodynamic Computing
Maxwell Aifer, Denis Melanson, Kaelan Donatella +3
While physics-based computing can offer speed and energy efficiency compared to digital computing, it also is subject to errors that must be mitigated. For example, many error miti…
Thermodynamic Computing System for AI Applications
Denis Melanson, Mohammad Abu Khater, Maxwell Aifer +7
Recent breakthroughs in artificial intelligence (AI) algorithms have highlighted the need for novel computing hardware in order to truly unlock the potential for AI. Physics-based…
Thermodynamic Matrix Exponentials and Thermodynamic Parallelism
Samuel Duffield, Maxwell Aifer, Gavin Crooks +2
Thermodynamic computing exploits fluctuations and dissipation in physical systems to efficiently solve various mathematical problems. For example, it was recently shown that certai…
Thermodynamic Linear Algebra
Maxwell Aifer, Kaelan Donatella, Max Hunter Gordon +5
Linear algebraic primitives are at the core of many modern algorithms in engineering, science, and machine learning. Hence, accelerating these primitives with novel computing hardw…