High-energy-efficiency binary neural network accelerator applicable to artificial intelligence internet of things
US11762700B2 · kind B2 · utility
Assignee
Inventors
Key dates
| Filing date | Jan 19, 2023 |
| Grant date | Sep 19, 2023 |
| Priority date | — |
| Expiry date | Jan 19, 2043 |
Classification
- Technology area (CPC Y)Emerging Cross-Sectional Technologies
- CPC primaryY02D10/00
- WIPO fieldComputer technology
- WIPO sectorElectrical engineering
Abstract
A high-energy-efficiency binary neural network accelerator applicable to artificial intelligence Internet of Things is provided. 0.3-0.6V sub/near threshold 10T1C multiplication bit units with series capacitors are configured for charge domain binary convolution. An anti-process deviation differential voltage amplification array between bit lines and DACs is configured for robust pre-amplification in 0.3V batch standardized operations. A lazy bit line reset scheme further reduces energy, and inference accuracy losses can be ignored. Therefore, a binary neural network accelerator chip based on in-memory computation achieves peak energy efficiency of 18.5 POPS/W and 6.06 POPS/W, which are respectively improved by 21× and 135× compared with previous macro and system work [9, 11].
Source: USPTO / EPO open patent data. Objective bibliographic and citation counts.