210 citations · 285 across the 5 of their papers we have counts for
5 papers
Novel Weight Update Scheme for Hardware Neural Network based on Synaptic Devices Having Abrupt LTP or LTD Characteristics
Junmo Lee, Joon Hwang, Youngwoon Cho +2
Mitigating nonlinear weight update characteristics is one of the main challenges in designing neural networks based on synaptic devices. This paper presents a novel weight update m…
Opportunities for Analog Coding in Emerging Memory Systems
Jesse H. Engel, S. Burc Eryilmaz, SangBum Kim +5
The exponential growth in data generation and large-scale data analysis creates an unprecedented need for inexpensive, low-latency, and high-density information storage. This need…
Training a Probabilistic Graphical Model with Resistive Switching Electronic Synapses
S. Burc Eryilmaz, Emre Neftci, Siddharth Joshi +6
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. Emerging…
Brain-like associative learning using a nanoscale non-volatile phase change synaptic device array
Sukru Burc Eryilmaz, Duygu Kuzum, Rakesh Jeyasingh +4
Recent advances in neuroscience together with nanoscale electronic device technology have resulted in huge interests in realizing brain-like computing hardwares using emerging nano…
Experimental Demonstration of Array-level Learning with Phase Change Synaptic Devices
S. Burc Eryilmaz, Duygu Kuzum, Rakesh G. D. Jeyasingh +4
The computational performance of the biological brain has long attracted significant interest and has led to inspirations in operating principles, algorithms, and architectures for…