no code implementations • 17 Aug 2021 • Weier Wan, Rajkumar Kubendran, Clemens Schaefer, S. Burc Eryilmaz, Wenqiang Zhang, Dabin Wu, Stephen Deiss, Priyanka Raina, He Qian, Bin Gao, Siddharth Joshi, Huaqiang Wu, H. -S. Philip Wong, Gert Cauwenberghs
Realizing today's cloud-level artificial intelligence functionalities directly on devices distributed at the edge of the internet calls for edge hardware capable of processing multiple modalities of sensory data (e. g. video, audio) at unprecedented energy-efficiency.
no code implementations • 15 Feb 2021 • Berivan Isik, Kristy Choi, Xin Zheng, Tsachy Weissman, Stefano Ermon, H. -S. Philip Wong, Armin Alaghi
Compression and efficient storage of neural network (NN) parameters is critical for applications that run on resource-constrained devices.
no code implementations • 23 Nov 2018 • Abbas Rahimi, Tony F. Wu, Haitong Li, Jan M. Rabaey, H. -S. Philip Wong, Max M. Shulaker, Subhasish Mitra
By exploiting the unique properties of the underlying nanotechnologies, we show that HD computing, when implemented with monolithic 3D integration, can be up to 420X more energy-efficient while using 25X less area compared to traditional silicon CMOS implementations.
no code implementations • 27 Sep 2016 • S. Burc Eryilmaz, Emre Neftci, Siddharth Joshi, Sang-Bum Kim, Matthew BrightSky, Hsiang-Lan Lung, Chung Lam, Gert Cauwenberghs, H. -S. Philip Wong
Current large scale implementations of deep learning and data mining require thousands of processors, massive amounts of off-chip memory, and consume gigajoules of energy.
no code implementations • 25 Dec 2015 • Sukru Burc Eryilmaz, Duygu Kuzum, Shimeng Yu, H. -S. Philip Wong
This paper gives an overview of recent progress in the brain inspired computing field with a focus on implementation using emerging memories as electronic synapses.
no code implementations • 19 Jun 2014 • Sukru Burc Eryilmaz, Duygu Kuzum, Rakesh Jeyasingh, Sang-Bum Kim, Matthew BrightSky, Chung Lam, H. -S. Philip Wong
Recent advances in neuroscience together with nanoscale electronic device technology have resulted in huge interests in realizing brain-like computing hardwares using emerging nanoscale memory devices as synaptic elements.
no code implementations • 29 May 2014 • S. Burc Eryilmaz, Duygu Kuzum, Rakesh G. D. Jeyasingh, Sang-Bum Kim, Matthew BrightSky, Chung Lam, H. -S. Philip Wong
We demonstrate, in hardware, that 2-D crossbar arrays of phase change synaptic devices can achieve associative learning and perform pattern recognition.