Proposal and Demonstration of Asynchronous Parallel Information Processing Architecture Inspired by Brain Information Dynamics

2021 Fiscal Year Final Research Report

• Project Area: Brain information dynamics underlying multi-area interconnectivity and parallel processing
• Project/Area Number: 17H06315
• Research Category: Grant-in-Aid for Scientific Research on Innovative Areas (Research in a proposed research area)
• Allocation Type: Single-year Grants
• Review Section: Complex systems
• Research Institution: Institute of Physical and Chemical Research
• Principal Investigator: Takahashi Koichi 国立研究開発法人理化学研究所, 生命機能科学研究センター, チームリーダー (20514508)
• Co-Investigator(Kenkyū-buntansha): 山川 宏 東京大学, 大学院医学系研究科(医学部), 客員研究員 (00417495)
• Project Period (FY): 2017-06-30 – 2022-03-31
• Keywords: 脳型人工知能 / 全脳アーキテクチャ / 並列計算 / 高性能計算

Outline of Final Research Achievements

Focusing on the interdomain coupling patterns of the brain, our research aimed to propose, implement, and demonstrate an asynchronous parallel information processing architecture inspired by the spatiotemporal hierarchical information dynamics of the brain. Regarding the brain-type reference architecture, we attempted to define (1) a neocortical master algorithm framework (MAF) for the neocortex. We also developed (2) a methodology for developing brain-type AI and a brain-type reference architecture for developing brain-type AI with reference to the coupled architecture among brain regions. In addition, to develop an execution infrastructure for brain-based asynchronous parallel computation, we developed (3) brain-based computation infrastructure software, BriCA, and examined (4) brain-based asynchronous parallel computation models for this demonstration.

Free Research Field

人工知能、計算システム生物学

Academic Significance and Societal Importance of the Research Achievements

脳情報動態の時空間的階層性は現代の計算アーキテクチャのプロセッサ、メモリ、ネットワーク階層における局所性との共通点も多く、分散並列アーキテクチャの観点から脳に学べる点は多い。本研究は将来的にはエネルギー効率が飛躍的に高い情報処理技術やIoTなどの情報通信ネットワークの効率化、高信頼性などに結びつく可能性がある。