12 citations · 12 across the 6 of their papers we have counts for
6 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…
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…
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…
TDO-CIM: Transparent Detection and Offloading for Computation In-memory
Kanishkan Vadivel, Lorenzo Chelini, Ali BanaGozar +4
Computation in-memory is a promising non-von Neumann approach aiming at completely diminishing the data transfer to and from the memory subsystem. Although a lot of architectures h…