Tootfinder

Fulcrum: Optimizing Concurrent DNN Training and Inferencing on Edge Accelerators
Prashanthi S. K., Saisamarth Taluri, Pranav Gupta, Amartya Ranjan Saikia, Kunal Kumar Sahoo, Atharva Vinay Joshi, Lakshya Karwa, Kedar Dhule, Yogesh Simmhan
https://arxiv.org/abs/2509.20205

Fulcrum: Optimizing Concurrent DNN Training and Inferencing on Edge Accelerators
The proliferation of GPU accelerated edge devices like Nvidia Jetsons and the rise in privacy concerns are placing an emphasis on concurrent DNN training and inferencing on edge devices. Inference and training have different computing and QoS goals. But edge accelerators like Jetson do not support native GPU sharing and expose 1000s of power modes. This requires careful time-sharing of concurrent workloads to meet power--performance goals, while limiting costly profiling. In this paper, we desi…

Pagoda: An Energy and Time Roofline Study for DNN Workloads on Edge Accelerators
Prashanthi S. K., Kunal Kumar Sahoo, Amartya Ranjan Saikia, Pranav Gupta, Atharva Vinay Joshi, Priyanshu Pansari, Yogesh Simmhan
https://arxiv.org/abs/2509.20189

Pagoda: An Energy and Time Roofline Study for DNN Workloads on Edge Accelerators
Edge accelerators such as Nvidia Jetsons are becoming an integral part of the computing continuum, and are often used for DNN inferencing and training. Nvidia Jetson edge devices have $2000$+ CUDA cores within a $70$W power envelope and offer $1000$s of power modes to customize CPU, GPU and memory frequencies. Their widely varying power--performance trade-offs can be exploited for energy and power-constrained deployments. While data-driven methods to predict the power and latency of DNN workloa…