5 papers
Multi-Level Modeling of Large Language Model Inference Latency and Energy via Hybrid Analytical--Machine-Learning Predictors
Saeid Shokoufa, Mohammad Erfan Sadeghi, Mehdi Kamal +1
The rapid scaling of Large Language Models (LLMs) has significantly increased computational cost, energy consumption, and inference latency, making accurate estimation essential fo…
VISTA: Vision-Language Inference for Training-Free Stock Time-Series Analysis
Tina Khezresmaeilzadeh, Parsa Razmara, Seyedarmin Azizi +2
Stock price prediction remains a complex and high-stakes task in financial analysis, traditionally addressed using statistical models or, more recently, language models. In this wo…
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…
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…
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…