16 citations · 24 across the 22 of their papers we have counts for
8 papers · 1 filter
SENMAP: Multi-objective data-flow mapping and synthesis for hybrid scalable neuromorphic systems
Prithvish V Nembhani, Oliver Rhodes, Guangzhi Tang +10
This paper introduces SENMap, a mapping and synthesis tool for scalable, energy-efficient neuromorphic computing architecture frameworks. SENECA is a flexible architectural design…
Efficient Synaptic Delay Implementation in Digital Event-Driven AI Accelerators
Roy Meijer, Paul Detterer, Amirreza Yousefzadeh +8
Synaptic delay parameterization of neural network models have remained largely unexplored but recent literature has been showing promising results, suggesting the delay parameteriz…
Explore Activation Sparsity in Recurrent LLMs for Energy-Efficient Neuromorphic Computing
Ivan Knunyants, Maryam Tavakol, Manolis Sifalakis +3
The recent rise of Large Language Models (LLMs) has revolutionized the deep learning field. However, the desire to deploy LLMs on edge devices introduces energy efficiency and late…
Exploring the Limitations of Layer Synchronization in Spiking Neural Networks
Roel Koopman, Amirreza Yousefzadeh, Mahyar Shahsavari +2
Neural-network processing in machine learning applications relies on layer synchronization. This is practiced even in artificial Spiking Neural Networks (SNNs), which are touted as…
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…
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…
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…