16 citations · 19 across the 6 of their papers we have counts for
6 papers
Event-based Optical Flow on Neuromorphic Processor: ANN vs. SNN Comparison based on Activation Sparsification
Yingfu Xu, Guangzhi Tang, Amirreza Yousefzadeh +2
Spiking neural networks (SNNs) for event-based optical flow are claimed to be computationally more efficient than their artificial neural networks (ANNs) counterparts, but a fair c…
TRIP: Trainable Region-of-Interest Prediction for Hardware-Efficient Neuromorphic Processing on Event-based Vision
Cina Arjmand, Yingfu Xu, Kevin Shidqi +6
Neuromorphic processors are well-suited for efficiently handling sparse events from event-based cameras. However, they face significant challenges in the growth of computing demand…
EON-1: A Brain-Inspired Processor for Near-Sensor Extreme Edge Online Feature Extraction
Alexandra Dobrita, Amirreza Yousefzadeh, Simon Thorpe +8
For Edge AI applications, deploying online learning and adaptation on resource-constrained embedded devices can deal with fast sensor-generated streams of data in changing environm…
Hardware-aware training of models with synaptic delays for digital event-driven neuromorphic processors
Alberto Patino-Saucedo, Roy Meijer, Amirreza Yousefzadeh +6
Configurable synaptic delays are a basic feature in many neuromorphic neural network hardware accelerators. However, they have been rarely used in model implementations, despite th…
Empirical study on the efficiency of Spiking Neural Networks with axonal delays, and algorithm-hardware benchmarking
Alberto Patiño-Saucedo, Amirreza Yousefzadeh, Guangzhi Tang +3
The role of axonal synaptic delays in the efficacy and performance of artificial neural networks has been largely unexplored. In step-based analog-valued neural network models (ANN…
Open the box of digital neuromorphic processor: Towards effective algorithm-hardware co-design
Guangzhi Tang, Ali Safa, Kevin Shidqi +6
Sparse and event-driven spiking neural network (SNN) algorithms are the ideal candidate solution for energy-efficient edge computing. Yet, with the growing complexity of SNN algori…