18 citations · 32 across the 8 of their papers we have counts for
4 papers · 1 filter
NeuroMorse: A Temporally Structured Dataset For Neuromorphic Computing
Ben Walters, Yeshwanth Bethi, Taylor Kergan +5
Neuromorphic engineering aims to advance computing by mimicking the brain's efficient processing, where data is encoded as asynchronous temporal events. This eliminates the need fo…
A Neuromorphic Architecture for Reinforcement Learning from Real-Valued Observations
Sergio F. Chevtchenko, Yeshwanth Bethi, Teresa B. Ludermir +1
Reinforcement Learning (RL) provides a powerful framework for decision-making in complex environments. However, implementing RL in hardware-efficient and bio-inspired ways remains…
Efficient Implementation of a Multi-Layer Gradient-Free Online-Trainable Spiking Neural Network on FPGA
Ali Mehrabi, Yeshwanth Bethi, André van Schaik +2
This paper presents an efficient hardware implementation of the recently proposed Optimized Deep Event-driven Spiking Neural Network Architecture (ODESA). ODESA is the first networ…
An optimised deep spiking neural network architecture without gradients
Yeshwanth Bethi, Ying Xu, Gregory Cohen +2
We present an end-to-end trainable modular event-driven neural architecture that uses local synaptic and threshold adaptation rules to perform transformations between arbitrary spa…