Please use this identifier to cite or link to this item: https://doi.org/10.1109/ISLPED.2013.6629290
Title: A practical low-power memristor-based analog neural branch predictor
Authors: Wang, J.
Tim, Y.
Wong, W.-F. 
Li, H.H.
Keywords: Branch Prediction
Memristor
Neural Branch Predictor
Issue Date: 2013
Source: Wang, J.,Tim, Y.,Wong, W.-F.,Li, H.H. (2013). A practical low-power memristor-based analog neural branch predictor. Proceedings of the International Symposium on Low Power Electronics and Design : 175-180. ScholarBank@NUS Repository. https://doi.org/10.1109/ISLPED.2013.6629290
Abstract: Recently, the discovery of memristor brought the promise of high density, low energy, and combined memory/arithmetic capability into computing. This paper demonstrates a practical neural branch predictor based on memristor. By using analog computation techniques, as well as exploiting the accuracy tolerance of branch prediction, our design is able to efficiently realize a neural prediction algorithm. Compared to the digital counterpart, our method achieves significant energy reduction while maintaining a better prediction accuracy and a higher IPC. Our approach also reduces the resource and energy required by an alternative design. © 2013 IEEE.
Source Title: Proceedings of the International Symposium on Low Power Electronics and Design
URI: http://scholarbank.nus.edu.sg/handle/10635/77974
ISBN: 9781479912353
ISSN: 15334678
DOI: 10.1109/ISLPED.2013.6629290
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