17 citations · 99 across the 66 of their papers we have counts for
12 papers · 1 filter
A Tunable Robust Pruning Framework Through Dynamic Network Rewiring of DNNs
Souvik Kundu, Mahdi Nazemi, Peter A. Beerel +1
This paper presents a dynamic network rewiring (DNR) method to generate pruned deep neural network (DNN) models that are robust against adversarial attacks yet maintain high accura…
SynergicLearning: Neural Network-Based Feature Extraction for Highly-Accurate Hyperdimensional Learning
Mahdi Nazemi, Amirhossein Esmaili, Arash Fayyazi +1
Machine learning models differ in terms of accuracy, computational/memory complexity, training time, and adaptability among other characteristics. For example, neural networks (NNs…
Deep-PowerX: A Deep Learning-Based Framework for Low-Power Approximate Logic Synthesis
Ghasem Pasandi, Mackenzie Peterson, Moises Herrera +2
This paper aims at integrating three powerful techniques namely Deep Learning, Approximate Computing, and Low Power Design into a strategy to optimize logic at the synthesis level.…
HIPE-MAGIC: A Technology-Aware Synthesis and Mapping Flow for HIghly Parallel Execution of Memristor-Aided LoGIC
Arash Fayyazi, Amirhossein Esmaili, Massoud Pedram
Recent efforts for finding novel computing paradigms that meet today's design requirements have given rise to a new trend of processing-in-memory relying on non-volatile memories.…
Logic Verification of Ultra-Deep Pipelined Beyond-CMOS Technologies
Arash Fayyazi, Shahin Nazarian, Massoud Pedram
Traditional logical equivalence checking (LEC) which plays a major role in entire chip design process faces challenges of meeting the requirements demanded by the many emerging tec…
NISQ+: Boosting quantum computing power by approximating quantum error correction
Adam Holmes, Mohammad Reza Jokar, Ghasem Pasandi +3
Quantum computers are growing in size, and design decisions are being made now that attempt to squeeze more computation out of these machines. In this spirit, we design a method to…