The Monte Carlo Method and New Device and Architectural Techniques for Accelerating It
Abstrak
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.
Topik & Kata Kunci
Penulis (8)
Janith Petangoda
Chatura Samarakoon
James Meech
Divya Thekke Kanapram
Hamid Toshani
Nathaniel Tye
Vasileios Tsoutsouras
Phillip Stanley-Marbell
Akses Cepat
- Tahun Terbit
- 2025
- Bahasa
- en
- Sumber Database
- arXiv
- Akses
- Open Access ✓