16 citations · 24 across the 22 of their papers we have counts for
22 papers
Recent Developments in Transformer Inference Deployment on FPGA Platforms: A Survey
Arjan Blankestijn, Uraz Odyurt, Amirreza Yousefzadeh
With the rapid and continuous growth in the incorporation of machine learning models based on the Transformer architecture, capable deployment is in high demand. In this context, c…
MergeOver: Post-Training Token Merging for Recursive Vision Transformers
Junseo Kim, Uraz Odyurt, Amirreza Yousefzadeh
Vision Transformers (ViTs) demonstrate exceptional performance in computer vision but suffer from large parameter counts and quadratic computational complexity, severely limiting t…
Reducing Instruction-Fetch Energy in RISC-V for Embedded AI Processing via Dynamic and Static Loop Caching
Wiebren Wijnstra, Sameed Sohail, Berend-Jan van der Zwaag +2
Embedded RISC-V processors are increasingly deployed for on-device AI inference at the edge, where energy efficiency is a primary design constraint. Instruction fetching from SRAM-…
The Sparsity Tax: Weight Sparsity Trade-offs in Event-Driven SIMD and SIMT Neuromorphic Cores
Mattias Westerink, Sameed Sohail, Berend-Jan van der Zwaag +2
Event-driven neuromorphic inference exploits activation sparsity by updating neuron state only on spikes. However, weight sparsity introduces irregular gather-style updates that un…
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…
Shooting Neutrons at Neurons: Radiation Testing of a Spiking Neural Network on Flash-Based FPGAs
Wim Nijsink, Bruno Endres Forlin, Amirreza Yousefzadeh +1
Neuromorphic, or spiking, processors are increasingly being considered for use in harsh, radiation-prone environments such as space and avionics, where energy efficiency and gracef…
Memory Wall is not gone: A Critical Outlook on Memory Architecture in Digital Neuromorphic Computing
Amirreza Yousefzadeh, Sameed Sohail, Ana Lucia Varbanescu
The rapid advancement of neuromorphic technology aims to address the memory wall challenge inherent in conventional von Neumann architectures. This paper critically examines curren…
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…
Energy-efficient SNN Architecture using 3nm FinFET Multiport SRAM-based CIM with Online Learning
Lucas Huijbregts, Liu Hsiao-Hsuan, Paul Detterer +3
Current Artificial Intelligence (AI) computation systems face challenges, primarily from the memory-wall issue, limiting overall system-level performance, especially for Edge devic…
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…
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…
Training for temporal sparsity in deep neural networks, application in video processing
Amirreza Yousefzadeh, Manolis Sifalakis
Activation sparsity improves compute efficiency and resource utilization in sparsity-aware neural network accelerators. As the predominant operation in DNNs is multiply-accumulate…