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2022 Fiscal Year Final Research Report

Development of a Probabilistic Sampling Machine for Spectral Decomposition using FPGA

Research Project

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Project/Area Number 19K12154
Research Category

Grant-in-Aid for Scientific Research (C)

Allocation TypeMulti-year Fund
Section一般
Review Section Basic Section 61040:Soft computing-related
Research InstitutionThe University of Electro-Communications

Principal Investigator

Shouno Hayaru  電気通信大学, 大学院情報理工学研究科, 教授 (50263231)

Project Period (FY) 2019-04-01 – 2023-03-31
Keywordsエッジデバイス / FPGA / MCMC法
Outline of Final Research Achievements

This project aims to verify the feasibility of implementing computationally expensive models on FPGAs. The main objectives of this project include evaluating computational costs and programming the Markov Chain Monte Carlo (MCMC) method for FPGA implementation. Specifically, we implemented a computational model based on the Ising spin model on an FPGA and applied the MCMC method to compute the states of this model. Additionally, to apply this method to spectral decomposition, we developed a computational algorithm that combines the temperature-exchange MCMC method and evolutionary computation for spectral decomposition.

Free Research Field

ソフトコンピューティング

Academic Significance and Societal Importance of the Research Achievements

省電力で計算コストが安価なFPGAはエッジデバイスとして,今後の計算機発展において重要な役割を果たすと考えられる.本研究ではFPGAを中心として,実問題へアプローチするために計算モデルを簡略化してどの程度の成果が得られるのかを試行している.その結果,計算モデルにおけるビット演算精度を落とすといった工夫を用いることにより,一定の計算精度を担保した形で,深層学習やマルコフチェーンモンテカルロ法などの計算手法がFPGA上で実現できることを示した.

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Published: 2024-01-30  

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