2023 Fiscal Year Final Research Report
Time-Space Re-configurable Flash Computations
Project/Area Number |
21K11809
|
Research Category |
Grant-in-Aid for Scientific Research (C)
|
Allocation Type | Multi-year Fund |
Section | 一般 |
Review Section |
Basic Section 60040:Computer system-related
|
Research Institution | Nara Institute of Science and Technology |
Principal Investigator |
ZHANG Renyuan 奈良先端科学技術大学院大学, 先端科学技術研究科, 准教授 (00709131)
|
Co-Investigator(Kenkyū-buntansha) |
木村 睦 奈良先端科学技術大学院大学, 先端科学技術研究科, 客員教授 (60368032)
|
Project Period (FY) |
2021-04-01 – 2024-03-31
|
Keywords | approximate computing / Neuromorphic circuits / stochastic computing / low power / artificial intelligence |
Outline of Final Research Achievements |
The approximate computing architectures are developed in this project, which are re-configurable in temporal or spatial domain. By using the proposed technologies, the hardware (HW) costs are greatly reduced with reasonable computing accuracy. For temporal re-configurability, a series of neuromorphic computing platforms on the basis of our original topology named “DiaNet” are proposed and verified for artificial neural networks (ANNs). From various validations, the proposed architectures reduce the use of HW resources up to 95% with similar quality of service as conventional works. For spatial reconfigurable computing architectures, the asynchronous stochastic computing (ASC) methodology is proposed, implemented, and validated by various arithmetic calculations. The ASC circuits are found superior to synchronous SC on hardware efficiency and speed with similar accuracy. Moreover, the ASC platform offers rich re-configurability to trade off performance and cost post silicon.
|
Free Research Field |
高性能近似計算技術
|
Academic Significance and Societal Importance of the Research Achievements |
The technologies developed in this project are found as promising candidates of post-Moore soft computing trends for accelerating the artificial intelligence tasks. This work explores the up limit of approximate computing and reasonable scenarios for it by cutting off a great processing energy.
|