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An Accurate, Error-Tolerant, and Energy-Efficient Neural Network Inference Engine Based on SONOS Analog Memory.
- Source :
-
IEEE Transactions on Circuits & Systems. Part I: Regular Papers . Apr2022, Vol. 69 Issue 4, p1480-1493. 14p. - Publication Year :
- 2022
-
Abstract
- We demonstrate SONOS (silicon-oxide-nitride-oxide-silicon) analog memory arrays that are optimized for neural network inference. The devices are fabricated in a 40nm process and operated in the subthreshold regime for in-memory matrix multiplication. Subthreshold operation enables low conductances to be implemented with low error, which matches the typical weight distribution of neural networks, which is heavily skewed toward near-zero values. This leads to high accuracy in the presence of programming errors and process variations. We simulate the end-to-end neural network inference accuracy, accounting for the measured programming error, read noise, and retention loss in a fabricated SONOS array. Evaluated on the ImageNet dataset using ResNet50, the accuracy using a SONOS system is within 2.16% of floating-point accuracy without any retraining. The unique error properties and high On/Off ratio of the SONOS device allow scaling to large arrays without bit slicing, and enable an inference architecture that achieves 20 TOPS/W on ResNet50, a $> 10\times $ gain in energy efficiency over state-of-the-art digital and analog inference accelerators. [ABSTRACT FROM AUTHOR]
Details
- Language :
- English
- ISSN :
- 15498328
- Volume :
- 69
- Issue :
- 4
- Database :
- Academic Search Index
- Journal :
- IEEE Transactions on Circuits & Systems. Part I: Regular Papers
- Publication Type :
- Periodical
- Accession number :
- 156247793
- Full Text :
- https://doi.org/10.1109/TCSI.2021.3134313