most citedFunctional Specification of the RAVENS Neuroprocessor

5 citations · 8 across the 7 of their papers we have counts for

collaborators

7 papers

cs.ET2023

Multi-level, Forming Free, Bulk Switching Trilayer RRAM for Neuromorphic Computing at the Edge

Jaeseoung Park, Ashwani Kumar, Yucheng Zhou +9

Resistive memory-based reconfigurable systems constructed by CMOS-RRAM integration hold great promise for low energy and high throughput neuromorphic computing. However, most RRAM…

cs.NE2023

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…

cs.NE20235 cited

Functional Specification of the RAVENS Neuroprocessor

Adam Z. Foshie, James S. Plank, Garrett S. Rose +1

RAVENS is a neuroprocessor that has been developed by the TENNLab research group at the University of Tennessee. Its main focus has been as a vehicle for chip design with memristiv…

cs.NE2023

On-Sensor Data Filtering using Neuromorphic Computing for High Energy Physics Experiments

Shruti R. Kulkarni, Aaron Young, Prasanna Date +12

This work describes the investigation of neuromorphic computing-based spiking neural network (SNN) models used to filter data from sensor electronics in high energy physics experim…

q-bio.NC2023

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…

cs.NE2022

Encoding Integers and Rationals on Neuromorphic Computers using Virtual Neuron

Prasanna Date, Shruti Kulkarni, Aaron Young +3

Neuromorphic computers perform computations by emulating the human brain, and use extremely low power. They are expected to be indispensable for energy-efficient computing in the f…