10 citations · 23 across the 11 of their papers we have counts for
11 papers
Rethinking Post-Training Quantization: Introducing a Statistical Pre-Calibration Approach
Alireza Ghaffari, Sharareh Younesian, Boxing Chen +2
As Large Language Models (LLMs) become increasingly computationally complex, developing efficient deployment strategies, such as quantization, becomes crucial. State-of-the-art Pos…
AdpQ: A Zero-shot Calibration Free Adaptive Post Training Quantization Method for LLMs
Alireza Ghaffari, Sharareh Younesian, Vahid Partovi Nia +2
The ever-growing computational complexity of Large Language Models (LLMs) necessitates efficient deployment strategies. The current state-of-the-art approaches for Post-training Qu…
OAC: Output-adaptive Calibration for Accurate Post-training Quantization
Ali Edalati, Alireza Ghaffari, Mahsa Ghazvini Nejad +4
Deployment of Large Language Models (LLMs) has major computational costs, due to their rapidly expanding size. Compression of LLMs reduces the memory footprint, latency, and energy…
Mitigating Outlier Activations in Low-Precision Fine-Tuning of Language Models
Alireza Ghaffari, Justin Yu, Mahsa Ghazvini Nejad +3
Low-precision fine-tuning of language models has gained prominence as a cost-effective and energy-efficient approach to deploying large-scale models in various applications. Howeve…
Statistical Hardware Design With Multi-model Active Learning
Alireza Ghaffari, Masoud Asgharian, Yvon Savaria
With the rising complexity of numerous novel applications that serve our modern society comes the strong need to design efficient computing platforms. Designing efficient hardware…
On the Convergence of Stochastic Gradient Descent in Low-precision Number Formats
Matteo Cacciola, Antonio Frangioni, Masoud Asgharian +2
Deep learning models are dominating almost all artificial intelligence tasks such as vision, text, and speech processing. Stochastic Gradient Descent (SGD) is the main tool for tra…
EuclidNets: An Alternative Operation for Efficient Inference of Deep Learning Models
Xinlin Li, Mariana Parazeres, Adam Oberman +3
With the advent of deep learning application on edge devices, researchers actively try to optimize their deployments on low-power and restricted memory devices. There are establish…
Towards Fine-tuning Pre-trained Language Models with Integer Forward and Backward Propagation
Mohammadreza Tayaranian, Alireza Ghaffari, Marzieh S. Tahaei +3
The large number of parameters of some prominent language models, such as BERT, makes their fine-tuning on downstream tasks computationally intensive and energy hungry. Previously…
Is Integer Arithmetic Enough for Deep Learning Training?
Alireza Ghaffari, Marzieh S. Tahaei, Mohammadreza Tayaranian +2
The ever-increasing computational complexity of deep learning models makes their training and deployment difficult on various cloud and edge platforms. Replacing floating-point ari…
Rethinking Pareto Frontier for Performance Evaluation of Deep Neural Networks
Vahid Partovi Nia, Alireza Ghaffari, Mahdi Zolnouri +1
Performance optimization of deep learning models is conducted either manually or through automatic architecture search, or a combination of both. On the other hand, their performan…
CNN2Gate: Toward Designing a General Framework for Implementation of Convolutional Neural Networks on FPGA
Alireza Ghaffari, Yvon Savaria
Convolutional Neural Networks (CNNs) have a major impact on our society because of the numerous services they provide. On the other hand, they require considerable computing power.…