17 citations · 33 across the 12 of their papers we have counts for
15 papers
NullaNet Tiny: Ultra-low-latency DNN Inference Through Fixed-function Combinational Logic
Mahdi Nazemi, Arash Fayyazi, Amirhossein Esmaili +3
While there is a large body of research on efficient processing of deep neural networks (DNNs), ultra-low-latency realization of these models for applications with stringent, sub-m…
BRDS: An FPGA-based LSTM Accelerator with Row-Balanced Dual-Ratio Sparsification
Seyed Abolfazl Ghasemzadeh, Erfan Bank Tavakoli, Mehdi Kamal +2
In this paper, first, a hardware-friendly pruning algorithm for reducing energy consumption and improving the speed of Long Short-Term Memory (LSTM) neural network accelerators is…
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.…