A Time-Domain Binary CNN Engine With Error-Detection-Based Resilience in 28nm CMOS
| Autor: | Yuxuan Du, Weiwei Shan, Xinchao Shang, Boyang Cheng, Zhikuang Cai |
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| Rok vydání: | 2021 |
| Předmět: | |
| Zdroj: | IEEE Transactions on Circuits and Systems II: Express Briefs. 68:3177-3181 |
| ISSN: | 1558-3791 1549-7747 |
| DOI: | 10.1109/tcsii.2021.3088857 |
| Popis: | Due to the increasing demand of high energy-efficient processor for deep neural networks, traditional neural network engines with high-precision weights and activations that usually occupies huge on/off-chip resources with large power consumption are no longer suitable for Internet-of-Things applications. Binary neural networks (BNNs) reduce memory size and computation complexity, achieving drastically increased energy efficiency. In this brief, an energy-efficient time-domain binary neural network engine is optimized for image recognition, with time-domain accumulation (TD-MAC), timing error detection based adaptive voltage scaling design and the related approximate computing. The proposed key features are: 1) an error-tolerant adaptive voltage scaling system with TD-MAC chain truncation for aggressive power reduction, working from near-threshold to normal voltage; 2) architectural parallelism and data reuse with 100% TD-MAC utilization; 3) low power TD-MAC based on analog delay lines. Fabricated in a 28nm CMOS process, the whole system achieves a maximum 51.5TOPS/W energy efficiency at 0.42V and 25MHz, with 99.6% accuracy on MNIST dataset. When the length of the TD-MAC chain is truncated by configuration, with a 90% accuracy on MNIST and a 150MHz, the proposed BNN achieves a power-saving of 13.2% and a further energy efficiency increasing of 67.6%. |
| Databáze: | OpenAIRE |
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