15 citations · 27 across the 5 of their papers we have counts for
5 papers
CEDD-optimizer: Enabling Cost-Efficient Dataset Distillation on Geographically Distributed Edge Systems
Dai Liu, Eishi Arima, Martin Schulz
Centralized learning is a fundamental paradigm in modern AI, in which data are collected from distributed edge devices and aggregated at a central host for model training. However,…
Optimizing Hardware Resource Partitioning and Job Allocations on Modern GPUs under Power Caps
Eishi Arima, Minjoon Kang, Issa Saba +3
CPU-GPU heterogeneous systems are now commonly used in HPC (High-Performance Computing). However, improving the utilization and energy-efficiency of such systems is still one of th…
Orchestrated Co-scheduling, Resource Partitioning, and Power Capping on CPU-GPU Heterogeneous Systems via Machine Learning
Issa Saba, Eishi Arima, Dai Liu +1
CPU-GPU heterogeneous architectures are now commonly used in a wide variety of computing systems from mobile devices to supercomputers. Maximizing the throughput for multi-programm…
Sustainability in HPC: Vision and Opportunities
Mohak Chadha, Eishi Arima, Amir Raoofy +2
Tackling climate change by reducing and eventually eliminating carbon emissions is a significant milestone on the path toward establishing an environmentally sustainable society. A…
Federated Learning via Decentralized Dataset Distillation in Resource-Constrained Edge Environments
Rui Song, Dai Liu, Dave Zhenyu Chen +4
In federated learning, all networked clients contribute to the model training cooperatively. However, with model sizes increasing, even sharing the trained partial models often lea…