363 citations · 412 across the 19 of their papers we have counts for
20 papers
Spike-based Neuromorphic Computing for Next-Generation Computer Vision
Md Sakib Hasan, Catherine D. Schuman, Zhongyang Zhang +2
Neuromorphic Computing promises orders of magnitude improvement in energy efficiency compared to traditional von Neumann computing paradigm. The goal is to develop an adaptive, fau…
A Deep Dive into the Design Space of a Dynamically Reconfigurable Cryogenic Spiking Neuron
Md Mazharul Islam, Shamiul Alam, Catherine D Schuman +2
Spiking neural network offers the most bio-realistic approach to mimic the parallelism and compactness of the human brain. A spiking neuron is the central component of an SNN which…
Enhanced Read Resolution in Reconfigurable Memristive Synapses for Spiking Neural Networks
Hritom Das, Nishith N. Chakraborty, Catherine Schuman +1
Synapse is a key element of any neuromorphic computing system which is mostly constructed with memristor devices. A memristor is a two-terminal analog memory device. Memristive syn…
Benchmarking the human brain against computational architectures
Céline van Valkenhoef, Catherine Schuman, Philip Walther
The human brain has inspired novel concepts complementary to classical and quantum computing architectures, such as artificial neural networks and neuromorphic computers, but it is…
Probabilistic Neural Circuits leveraging AI-Enhanced Codesign for Random Number Generation
Suma G. Cardwell, Catherine D. Schuman, J. Darby Smith +7
Stochasticity is ubiquitous in the world around us. However, our predominant computing paradigm is deterministic. Random number generation (RNG) can be a computationally inefficien…
Random Bitstream Generation using Voltage-Controlled Magnetic Anisotropy and Spin Orbit Torque Magnetic Tunnel Junctions
Samuel Liu, Jaesuk Kwon, Paul W. Bessler +6
Probabilistic computing using random number generators (RNGs) can leverage the inherent stochasticity of nanodevices for system-level benefits. The magnetic tunnel junction (MTJ) h…