93 citations · 199 across the 28 of their papers we have counts for
8 papers · 1 filter
Gradient Under Microscope: Benchmarking Resource Utilization of Memory-Efficient Gradient Computation Methods
Sarthak Mahapatra, Zihan Zhou, Khatoon Khedri +2
AI training's rising resource intensity is straining electricity supplies and carbon budgets, motivating systematic study of memory-efficient training on constrained hardware. We b…
NeuroViz: Real-time Interactive Visualization of Forward and Backward Passes in Neural Network Training
Tanvi Sharma, Reza Rawassizadeh
Training neural networks is difficult to interpret, particularly for newcomers. We introduce NeuroViz, an interactive visualization tool that supports real-time exploration of full…
Pruning and Quantization Impact on Graph Neural Networks
Khatoon Khedri, Reza Rawassizadeh, Qifu Wen +1
Graph neural networks (GNNs) are known to operate with high accuracy on learning from graph-structured data, but they suffer from high computational and resource costs. Neural netw…
GradES: Significantly Faster Training in Transformers with Gradient-Based Early Stopping
Qifu Wen, Xi Zeng, Zihan Zhou +4
Early stopping monitors global validation loss and halts all parameter updates simultaneously, which is computationally costly for large transformers due to the extended time requi…
Attentions Under the Microscope: A Comparative Study of Resource Utilization for Variants of Self-Attention
Zhengyu Tian, Anantha Padmanaban Krishna Kumar, Hemant Krishnakumar +1
As large language models (LLMs) and visual language models (VLMs) grow in scale and application, attention mechanisms have become a central computational bottleneck due to their hi…
The Impact of Quantization and Pruning on Deep Reinforcement Learning Models
Heng Lu, Mehdi Alemi, Reza Rawassizadeh
Deep reinforcement learning (DRL) has achieved remarkable success across various domains, such as video games, robotics, and, recently, large language models. However, the computat…
FEDZIP: A Compression Framework for Communication-Efficient Federated Learning
Amirhossein Malekijoo, Mohammad Javad Fadaeieslam, Hanieh Malekijou +3
Federated Learning marks a turning point in the implementation of decentralized machine learning (especially deep learning) for wireless devices by protecting users' privacy and sa…
SEFR: A Fast Linear-Time Classifier for Ultra-Low Power Devices
Hamidreza Keshavarz, Mohammad Saniee Abadeh, Reza Rawassizadeh
A fundamental challenge for running machine learning algorithms on battery-powered devices is the time and energy limitations, as these devices have constraints on resources. There…