Statistical machine learning often uses probabilistic models and algorithms, such as Markov Chain Monte Carlo (MCMC), to solve a wide range of problems. Probabilistic computations, often considered too slow on conventional processors, can be accelerated with specialized hardware by exploiting parallelism and optimizing the design using various approximation techniques. Current methodologies for evaluating correctness of probabilistic accelerators are often incomplete, mostly focusing only on end-point result quality ("accuracy"). It is important for hardware designers and domain experts to look beyond end-point "accuracy" and be aware of how hardware optimizations impact statistical properties.

The increasing difficulty in leveraging CMOS scaling for improved performance requires exploring alternative technologies. A promising technique is to exploit the physical properties of devices to specialize certain computations. A recently proposed approach uses molecular-scale optical devices to construct a Resonance Energy based Sampling Unit (RSU) to accelerate sampling from parameterized probability distributions. Sampling is an important component of many algorithms, including statistical machine learning. This paper explores the relationship between application result quality and RSU design. The previously proposed RSU-G focuses on Gibbs sampling using Markov Chain Monte Carlo (MCMC) solvers for Markov Random Field (MRF) Bayesian Inference. By quantitatively analyzing the result quality across three computer vision applications, we find that the previously proposed RSU-G lacks both sufficient precision and dynamic range in key design parameters, which limits the overall result quality compared to software-only MCMC implementations. Naively scaling the problematic parameters to increase precision and dynamic range consumes too much area and power. Therefore, we introduce a new RSU-G microarchitecture that exploits an alternative approach to increase precision that incurs 1.27x power and equivalent area, while maintaining the significant speedups of the previous design and supporting a wider set of applications.

The increasing use of probabilistic algorithms from statistics and machine learning for data analytics presents new challenges and opportunities for the design of computing systems. One important class of probabilistic machine learning algorithms is Markov Chain Monte Carlo (MCMC) sampling, which can be used on a wide variety of applications in Bayesian Inference. However, this probabilistic iterative algorithm can be inefficient in practice on today's processors, especially for problems with high dimensionality and complex structure. The source of inefficiency is generating samples from parameterized probability distributions. This paper seeks to address this sampling inefficiency and presents a new approach to support probabilistic computing that leverages the native randomness of Resonance Energy Transfer (RET) networks to construct RET-based sampling units (RSU). Although RSUs can be designed for a variety of applications, we focus on the specific class of probabilistic problems described as Markov Random Field Inference. Our proposed RSU uses a RET network to implement a molecular-scale optical Gibbs sampling unit (RSU-G) that can be integrated into a processor / GPU as specialized functional units or organized as a discrete accelerator. We experimentally demonstrate the fundamental operation of an RSU using a macro-scale hardware prototype. Emulation-based evaluation of two computer vision applications for HD images reveal that an RSU augmented GPU provides speedups over a GPU of 3 and 16. Analytic evaluation shows a discrete accelerator that is limited by 336 GB/s DRAM produces speedups of 21 and 54 versus the GPU implementations.