AMARA: Compiler-Driven Accelerator Architecture Design for Graphical Probabilistic Sampling

Sampling methods are fundamental primitives for training and inference on probabilistic models. However, practical deployment of sampling algorithms such as Markov Chain Monte Carlo (MCMC) remains difficult on general-purpose processors. Existing hardware accelerators improve sampling efficiency, but they are typically specialized for a narrow class of models, graph topologies, or update schedules, making it difficult to support the diverse workloads used in real applications. This paper presen…

Source: arXiv cs.AR (Hardware Architecture), published 2026-11-13. The summary is the Hub's own; the full story is at the source. Report a rights or accuracy concern