62 citations · 91 across the 17 of their papers we have counts for
26 papers
HIRE-SNN: Harnessing the Inherent Robustness of Energy-Efficient Deep Spiking Neural Networks by Training with Crafted Input Noise
Souvik Kundu, Massoud Pedram, Peter A. Beerel
Low-latency deep spiking neural networks (SNNs) have become a promising alternative to conventional artificial neural networks (ANNs) because of their potential for increased energ…
Towards Low-Latency Energy-Efficient Deep SNNs via Attention-Guided Compression
Souvik Kundu, Gourav Datta, Massoud Pedram +1
Deep spiking neural networks (SNNs) have emerged as a potential alternative to traditional deep learning frameworks, due to their promise to provide increased compute efficiency on…
Coarse2Fine: A Two-stage Training Method for Fine-grained Visual Classification
Amir Erfan Eshratifar, David Eigen, Michael Gormish +1
Small inter-class and large intra-class variations are the main challenges in fine-grained visual classification. Objects from different classes share visually similar structures a…
Optimizing Routerless Network-on-Chip Designs: An Innovative Learning-Based Framework
Ting-Ru Lin, Drew Penney, Massoud Pedram +1
Machine learning applied to architecture design presents a promising opportunity with broad applications. Recent deep reinforcement learning (DRL) techniques, in particular, enable…
Energy-Aware Scheduling of Task Graphs with Imprecise Computations and End-to-End Deadlines
Amirhossein Esmaili, Mahdi Nazemi, Massoud Pedram
Imprecise computations provide an avenue for scheduling algorithms developed for energy-constrained computing devices by trading off output quality with the utilization of system r…
VeriSFQ - A Semi-formal Verification Framework and Benchmark for Single Flux Quantum Technology
Alvin D. Wong, Kevin Su, Hang Sun +3
In this paper, we propose a semi-formal verification framework for single-flux quantum (SFQ) circuits called VeriSFQ, using the Universal Verification Methodology (UVM) standard. T…