4 citations · 6 across the 5 of their papers we have counts for
5 papers
Chiplet-Gym: Optimizing Chiplet-based AI Accelerator Design with Reinforcement Learning
Kaniz Mishty, Mehdi Sadi
Modern Artificial Intelligence (AI) workloads demand computing systems with large silicon area to sustain throughput and competitive performance. However, prohibitive manufacturing…
SNNLP: Energy-Efficient Natural Language Processing Using Spiking Neural Networks
R. Alexander Knipper, Kaniz Mishty, Mehdi Sadi +1
As spiking neural networks receive more attention, we look toward applications of this computing paradigm in fields other than computer vision and signal processing. One major fiel…
System and Design Technology Co-optimization of SOT-MRAM for High-Performance AI Accelerator Memory System
Kaniz Mishty, Mehdi Sadi
SoCs are now designed with their own AI accelerator segment to accommodate the ever-increasing demand of Deep Learning (DL) applications. With powerful MAC engines for matrix multi…
Attacking Deep Learning AI Hardware with Universal Adversarial Perturbation
Mehdi Sadi, B. M. S. Bahar Talukder, Kaniz Mishty +1
Universal Adversarial Perturbations are image-agnostic and model-independent noise that when added with any image can mislead the trained Deep Convolutional Neural Networks into th…
Designing Efficient and High-performance AI Accelerators with Customized STT-MRAM
Kaniz Mishty, Mehdi Sadi
In this paper, we demonstrate the design of efficient and high-performance AI/Deep Learning accelerators with customized STT-MRAM and a reconfigurable core. Based on model-driven d…