211 citations · 530 across the 27 of their papers we have counts for
5 papers · 1 filter
Training Spiking Neural Networks Using Lessons From Deep Learning
Jason K. Eshraghian, Max Ward, Emre Neftci +6
The brain is the perfect place to look for inspiration to develop more efficient neural networks. The inner workings of our synapses and neurons provide a glimpse at what the futur…
FPGA Synthesis of Ternary Memristor-CMOS Decoders
Xiaoyuan Wang, Zhiru Wu, Pengfei Zhou +3
The search for a compatible application of memristor-CMOS logic gates has remained elusive, as the data density benefits are offset by slow switching speeds and resistive dissipati…
Memristive Stochastic Computing for Deep Learning Parameter Optimization
Corey Lammie, Jason K. Eshraghian, Wei D. Lu +1
Stochastic Computing (SC) is a computing paradigm that allows for the low-cost and low-power computation of various arithmetic operations using stochastic bit streams and digital l…
Naturalizing Neuromorphic Vision Event Streams Using GANs
Dennis Robey, Wesley Thio, Herbert Iu +1
Dynamic vision sensors are able to operate at high temporal resolutions within resource constrained environments, though at the expense of capturing static content. The sparse natu…
CrossStack: A 3-D Reconfigurable RRAM Crossbar Inference Engine
Jason K. Eshraghian, Kyoungrok Cho, Sung Mo Kang
Deep neural network inference accelerators are rapidly growing in importance as we turn to massively parallelized processing beyond GPUs and ASICs. The dominant operation in feedfo…