AIA: A 16nm Multicore SoC for Approximate Inference Acceleration Exploiting Non-normalized Knuth-Yao Sampling and Inter-Core Register Sharing (opens in new tab)

Probabilistic graphical models (PMs) are popular to empower machine learning with the ability of reasoning and decision-making. To perform approximate inference in PMs, sampling-based Markov Chain Monte Carlo (MCMC) algorithms are commonly employed. Unfortunately, MCMC is compute-intensive and hard to run in parallel, resulting in inefficient execution on modern CPU/GPU platforms. This paper proposes \name{}, an Approximate Inference Accelerator...