2 citations · 2 across the 10 of their papers we have counts for
10 papers
Spiking and Event-driven Neuromorphic Mamba Models for Efficient Speech Recognition
Tauseef Ahmed, Tao Sun, Jeronimo Castrillon +2
Deep learning has greatly advanced automatic speech recognition (ASR), enabling widespread deployment on edge devices such as smartphones and smart home systems. However, the compu…
NERVE: A Neuromorphic Vision and Radar Ensemble for Multi-Sensor Fusion Research
Omar Mansour, Pietro Martinello, Ethan Milon +4
We present NERVE (Neuromorphic Vision and Radar Ensemble), a multi-sensor dataset comprising 257 minutes of synchronized recordings from five sensors: two Dynamic Vision Sensors (D…
Sparse Convolutional Recurrent Learning for Efficient Event-based Neuromorphic Object Detection
Shenqi Wang, Yingfu Xu, Amirreza Yousefzadeh +4
Leveraging the high temporal resolution and dynamic range, object detection with event cameras can enhance the performance and safety of automotive and robotics applications in rea…
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…
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…