16 citations · 24 across the 22 of their papers we have counts for
6 papers · 1 filter
From RISC-V Cores to Neuromorphic Arrays: A Tutorial on Building Scalable Digital Neuromorphic Processors
Amirreza Yousefzadeh
Digital neuromorphic processors are emerging as a promising computing substrate for low-power, always-on EdgeAI applications. In this tutorial paper, we outline the main architectu…
TrackCore-F: Deploying Transformer-Based Subatomic Particle Tracking on FPGAs
Arjan Blankestijn, Uraz Odyurt, Amirreza Yousefzadeh
The Transformer Machine Learning (ML) architecture has been gaining considerable momentum in recent years. In particular, computational High-Energy Physics tasks such as jet taggin…
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…