16 citations · 24 across the 22 of their papers we have counts for
7 papers · 1 filter
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…