Accelerating Markov Random Field Inference with Uncertainty Quantification

no code implementations • 2 Aug 2021 • Ramin Bashizade, Xiangyu Zhang, Sayan Mukherjee, Alvin R. Lebeck

In this paper, we propose a high-throughput accelerator for Markov Random Field (MRF) inference, a powerful model for representing a wide range of applications, using MCMC with Gibbs sampling.

Motion Estimation Playing the Game of 2048 +1

Beyond Application End-Point Results: Quantifying Statistical Robustness of MCMC Accelerators

no code implementations • 5 Mar 2020 • Xiangyu Zhang, Ramin Bashizade, Yicheng Wang, Cheng Lyu, Sayan Mukherjee, Alvin R. Lebeck

Applying the framework to guide design space exploration shows that statistical robustness comparable to floating-point software can be achieved by slightly increasing the bit representation, without floating-point hardware requirements.