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.
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.
no code implementations • 27 Oct 2019 • Xiangyu Zhang, Sayan Mukherjee, Alvin R. Lebeck
Although a common approach is to compare the end-point result quality using community-standard benchmarks and metrics, we claim a probabilistic architecture should provide some measure (or guarantee) of statistical robustness.