Search Results for author:
Found 3 papers, 0 papers with code
Accelerating Markov Random Field Inference with Uncertainty Quantification
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.
Beyond Application End-Point Results: Quantifying Statistical Robustness of MCMC Accelerators
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.
A Case for Quantifying Statistical Robustness of Specialized Probabilistic AI Accelerators
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.
