no code implementations • ICLR 2019 • Jun Haeng Lee, Sangwon Ha, Saerom Choi, Won-Jo Lee, Seungwon Lee
This paper aims at rapid deployment of the state-of-the-art deep neural networks (DNNs) to energy efficient accelerators without time-consuming fine tuning or the availability of the full datasets.
no code implementations • 12 Oct 2018 • Hyunsun Park, Jun Haeng Lee, Youngmin Oh, Sangwon Ha, Seungwon Lee
Energy and resource efficient training of DNNs will greatly extend the applications of deep learning.
no code implementations • ICML 2017 • Daniel Neil, Jun Haeng Lee, Tobi Delbruck, Shih-Chii Liu
Similarly, on the large Wall Street Journal speech recognition benchmark even existing networks can be greatly accelerated as delta networks, and a 5. 7x improvement with negligible loss of accuracy can be obtained through training.
no code implementations • 31 Aug 2016 • Jun Haeng Lee, Tobi Delbruck, Michael Pfeiffer
Deep spiking neural networks (SNNs) hold great potential for improving the latency and energy efficiency of deep neural networks through event-based computation.