The Monte Carlo Method and New Device and Architectural Techniques for Accelerating It

TL;DR

This paper introduces a framework for Monte Carlo methods, advances in non-uniform random variate generators, and architectural techniques that natively compute on probability distributions, reducing reliance on traditional sampling.

Contribution

It presents a new framework for describing Monte Carlo methods, advances in physics-based variate generators, and novel architectural techniques that eliminate the need for Monte Carlo sampling.

Findings

New framework for Monte Carlo description

Improved physics-based variate generators

Architectural techniques for native probability computation

Abstract

Computing systems interacting with real-world processes must safely and reliably process uncertain data. The Monte Carlo method is a popular approach for computing with such uncertain values. This article introduces a framework for describing the Monte Carlo method and highlights two advances in the domain of physics-based non-uniform random variate generators (PPRVGs) to overcome common limitations of traditional Monte Carlo sampling. This article also highlights recent advances in architectural techniques that eliminate the need to use the Monte Carlo method by leveraging distributional microarchitectural state to natively compute on probability distributions. Unlike Monte Carlo methods, uncertainty-tracking processor architectures can be said to be convergence-oblivious.