17 citations · 99 across the 66 of their papers we have counts for
11 papers · 1 filter
SAIM: Scalable Analog Ising Machine for Solving Quadratic Binary Optimization Problems
Sasan Razmkhah, Jui-Yu Huang, Mehdi Kamal +1
This paper presents a CMOS-compatible Lechner-Hauke-Zoller (LHZ)--based analog tile structure as a fundamental unit for developing scalable analog Ising machines (IMs). In the desi…
MENAGE: Mixed-Signal Event-Driven Neuromorphic Accelerator for Edge Applications
Armin Abdollahi, Mehdi Kamal, Massoud Pedram
This paper presents a mixed-signal neuromorphic accelerator architecture designed for accelerating inference with event-based neural network models. This fully CMOS-compatible acce…
Efficient Noise Mitigation for Enhancing Inference Accuracy in DNNs on Mixed-Signal Accelerators
Seyedarmin Azizi, Mohammad Erfan Sadeghi, Mehdi Kamal +1
In this paper, we propose a framework to enhance the robustness of the neural models by mitigating the effects of process-induced and aging-related variations of analog computing c…
Enhancing Layout Hotspot Detection Efficiency with YOLOv8 and PCA-Guided Augmentation
Dongyang Wu, Siyang Wang, Mehdi Kamal +1
In this paper, we present a YOLO-based framework for layout hotspot detection, aiming to enhance the efficiency and performance of the design rule checking (DRC) process. Our appro…
CHOSEN: Compilation to Hardware Optimization Stack for Efficient Vision Transformer Inference
Mohammad Erfan Sadeghi, Arash Fayyazi, Suhas Somashekar +2
Vision Transformers (ViTs) represent a groundbreaking shift in machine learning approaches to computer vision. Unlike traditional approaches, ViTs employ the self-attention mechani…
Dynamic Co-Optimization Compiler: Leveraging Multi-Agent Reinforcement Learning for Enhanced DNN Accelerator Performance
Arya Fayyazi, Mehdi Kamal, Massoud Pedram
This paper introduces a novel Dynamic Co-Optimization Compiler (DCOC), which employs an adaptive Multi-Agent Reinforcement Learning (MARL) framework to enhance the efficiency of ma…
LaMDA: Large Model Fine-Tuning via Spectrally Decomposed Low-Dimensional Adaptation
Seyedarmin Azizi, Souvik Kundu, Massoud Pedram
Low-rank adaptation (LoRA) has become the default approach to fine-tune large language models (LLMs) due to its significant reduction in trainable parameters. However, trainable pa…
Superconductor bistable vortex memory for data storage and in-memory computing
Mustafa Altay Karamuftuoglu, Beyza Zeynep Ucpinar, Sasan Razmkhah +1
Superconductor electronics (SCE) is a promising complementary and beyond CMOS technology. However, despite its practical benefits, the realization of SCE logic faces a significant…
PEANO-ViT: Power-Efficient Approximations of Non-Linearities in Vision Transformers
Mohammad Erfan Sadeghi, Arash Fayyazi, Seyedarmin Azizi +1
The deployment of Vision Transformers (ViTs) on hardware platforms, specially Field-Programmable Gate Arrays (FPGAs), presents many challenges, which are mainly due to the substant…
Scalable Superconductor Neuron with Ternary Synaptic Connections for Ultra-Fast SNN Hardware
Mustafa Altay Karamuftuoglu, Beyza Zeynep Ucpinar, Arash Fayyazi +3
A novel high-fan-in differential superconductor neuron structure designed for ultra-high-performance Spiking Neural Network (SNN) accelerators is presented. Utilizing a high-fan-in…
Memory-Efficient Vision Transformers: An Activation-Aware Mixed-Rank Compression Strategy
Seyedarmin Azizi, Mahdi Nazemi, Massoud Pedram
As Vision Transformers (ViTs) increasingly set new benchmarks in computer vision, their practical deployment on inference engines is often hindered by their significant memory band…