@inproceedings{3ebd86a4af2c487e9be440676a7842ee,
title = "SENIN: An energy-efficient sparse neuromorphic system with on-chip learning",
abstract = "Applying highly accurate neural networks to mobile devices encounters energy problems in battery-limited mobile environments. To resolve these problems, neuromorphic hardware solutions that enable event-driven operation have been proposed. In this work, we present a novel sparse neuromorphic system that implements an E-I Net algorithm to further improve energy efficiency. We introduce a neuron clock-gating technique that significantly reduces energy consumption by predicting future neuron spike activity without any loss of accuracy. We also propose synaptic pruning to save additional energy with minimal impact on classification accuracy. For fast adaptation to a changing environment, a learning algorithm is implemented in the proposed system. Compared to prior studies, our experimental results illustrate that the proposed system achieves 5.3×-11.4× energy efficiency improvement with comparable accuracy.",
keywords = "Neural Network, Neuromorphic Computing, Sparse Spike",
author = "Choi, {Myung Hoon} and Seungkyu Choi and Jaehyeong Sim and Kim, {Lee Sup}",
note = "Funding Information: This work was supported by the National Research Foundation of Korea(NRF) grant funded by the Korea government(MSIP) (NO. 2017R1A2B2009380) 978-1-5090-6023-8/17/$31.00 {\textcopyright} 2017 IEEE Publisher Copyright: {\textcopyright} 2017 IEEE.; 22nd IEEE/ACM International Symposium on Low Power Electronics and Design, ISLPED 2017 ; Conference date: 24-07-2017 Through 26-07-2017",
year = "2017",
month = aug,
day = "11",
doi = "10.1109/ISLPED.2017.8009174",
language = "English",
series = "Proceedings of the International Symposium on Low Power Electronics and Design",
publisher = "Institute of Electrical and Electronics Engineers Inc.",
booktitle = "ISLPED 2017 - IEEE/ACM International Symposium on Low Power Electronics and Design",
}