CoopMC: Algorithm-Architecture Co-Optimization for Markov Chain Monte Carlo Accelerators

Yuji Chai, Glenn G. Ko, Wei-Te Mark Ting, Luke Bailey, David Brooks, Gu-Yeon Wei

Abstract: Bayesian machine learning is useful for applications that may make high-risk decisions with limited, noisy, or unlabeled data, as it provides great data efficiency and uncertainty estimation. Building on previous efforts, this work presents CoopMC, an algorithm-architecture co-optimization for developing more efficient MCMC-based Bayesian inference accelerators. CoopMC utilizes dynamic normalization (DyNorm), LUT-based exponential kernels (TableExp), and log-domain kernel fusion (LogFusion) to reduce computational precision and shrink ALU area by 7.5× without noticeable reduction in model performance. Also, a Tree-based Gibbs sampler (TreeSampler) improves hardware runtime from $\mathcal{O}$(N) to $\mathcal{O}$(log(N)), an 8.7× speedup, and yields 1.9× better area efficiency than the existing state-of-the-art Gibbs sampling architecture. These methods have been tested on 10 diverse workloads using 3 different types of Bayesian models, demonstrating applicability to many Bayesian algorithms. In an end-to-end case study, these optimizations achieve a 33% logic area reduction, 62% power reduction, and 1.53× speedup over previous state-of-the-art end-to-end MCMC accelerators.