Probabilistic Compute-in-Memory Design for Efficient Markov Chain Monte Carlo Sampling-Reference-Cited by-同舟云学术

Probabilistic Compute-in-Memory Design for Efficient Markov Chain Monte Carlo Sampling

Published:2024-02 Issue:2 Volume:71 Page:703-716
ISSN:1549-8328
Container-title:IEEE Transactions on Circuits and Systems I: Regular Papers
language:
Short-container-title:IEEE Trans. Circuits Syst. I

Author:

Fu Yihan¹,Shi Daijing¹,Fan Anjunyi¹,Yue Wenshuo¹,Yang Yuchao¹^ORCID,Huang Ru¹,Yan Bonan¹^ORCID

Affiliation:

1. School of Integrated Circuits, Institute for Artificial Intelligence and the Beijing Advanced Innovation Center for Integrated Circuits, Peking University, Beijing, China

Funder

Natural Science Foundation of China

Higher Education Discipline Innovation Project

Fok Ying Tong Education Foundation

Tencent Foundation through the Xplorer Prize

Publisher

Institute of Electrical and Electronics Engineers (IEEE)

Subject

Electrical and Electronic Engineering,Hardware and Architecture

Link

http://xplorestaging.ieee.org/ielx7/8919/10416295/10347275.pdf?arnumber=10347275

Reference45 articles.

1. An introduction to MCMC for machine learning;Andrieu;Mach. Learn.,2003

2. Markov Chain Sampling Methods for Dirichlet Process Mixture Models

3. An Overview of MCMC Methods: From Theory to Applications

4. Probabilistic machine learning and artificial intelligence

5. Multi-agent reinforcement learning accelerated MCMC on multiscale inversion problem;Chung;arXiv:2011.08954,2020

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