Srinivasan Subramaniyan

805 Dreese Laboratories

2015 Neil Avenue

Columbus, Ohio 43201

I’m a Ph.D. candidate in Electrical and Computer Engineering at The Ohio State University, working in the Power-Aware Computer Systems (PACS) Lab under Dr. Xiaorui Wang.

My research focuses on GPU scheduling, computer architecture, and edge/cloud systems, with an emphasis on energy-efficient and high-performance computing. I am the author of several award-winning papers, including the Outstanding Paper Award at EMSOFT 2025 and the Best Paper Award at VLSID 2022. I am actively seeking opportunities in computer architecture, high-performance computing, and systems research, with a strong interest in developing next-generation energy-efficient computing architectures.

news

Dec 08, 2025	I have passed my candidacy exam and am officially a PhD candidate.
Nov 22, 2025	Excited to announce that i received the Best-paper runner up for the work “Exploiting ML Task Correlation in the Minimization of Capital Expense for GPU Data Centers.” at IPCCC 2025
Oct 27, 2025	Our paper “SEEB-GPU” has been accepted for publication at SEC 2025.
Oct 01, 2025	I presented my paper at ESWEEK (EMSOFT) 2025, where it was selected as a Best Paper Candidate.

selected publications

SEC

SEEB-GPU: Early-Exit Aware Scheduling and Batching for Edge GPU Inference

Srinivasan Subramaniyan, Rudra Joshi, Xiaorui Wang, and Marco Brocanelli

In Proceedings of the Tenth ACM/IEEE Symposium on Edge Computing, 2025

Bib DOI PDF Slides

@inproceedings{10.1145/3769102.3772715,
  author = {Subramaniyan, Srinivasan and Joshi, Rudra and Wang, Xiaorui and Brocanelli, Marco},
  title = {SEEB-GPU: Early-Exit Aware Scheduling and Batching for Edge GPU Inference},
  year = {2025},
  isbn = {9798400722387},
  publisher = {Association for Computing Machinery},
  address = {New York, NY, USA},
  url = {https://doi.org/10.1145/3769102.3772715},
  doi = {10.1145/3769102.3772715},
  booktitle = {Proceedings of the Tenth ACM/IEEE Symposium on Edge Computing},
  articleno = {9},
  numpages = {16},
  keywords = {edge inference, GPU resource management, early-exit DNNs},
  series = {SEC '25},
}

IPCCC

Exploiting ML Task Correlation in the Minimization of Capital Expense for GPU Data Centers

Srinivasan Subramaniyan and Xiaorui Wang

In Proceedings of the 44th IEEE International Performance Computing and Communications Conference, 2025

Best Paper Runner up award Bib PDF Slides

Recieved the Best Paper Runner up award

@inproceedings{CorrGPU,
  author = {Subramaniyan, Srinivasan and Wang, Xiaorui},
  title = {Exploiting ML Task Correlation in the Minimization of Capital Expense for GPU Data Centers},
  year = {2025},
  publisher = {IEEE},
  url = {https://par.nsf.gov/biblio/10652009},
  booktitle = {Proceedings of the 44th IEEE International Performance Computing and Communications Conference},
}

EMSOFT
FC-GPU: Feedback Control GPU Scheduling for Real-Time Embedded Systems

Srinivasan Subramaniyan and Xiaorui Wang

ACM Transactions on Embedded Computing Systems, Sep 2025

Outstanding Paper Bib DOI PDF Code Slides

Recieved the Outstanding Paper award for FC-GPU at ESWEEK 2025

GPUs have recently been adopted in many real-time embedded systems. However, existing GPU scheduling solutions are mostly open-loop and rely on the estimation of worst-case execution time (WCET). Although adaptive solutions, such as feedback control scheduling, have been previously proposed to handle this challenge for CPU-based real-time tasks, they cannot be directly applied to GPUs, because GPUs have different and more complex architectures and so schedulable utilization bounds cannot apply to GPUs yet. In this article, we propose FC-GPU, the first feedback control GPU scheduling framework for real-time embedded systems. To model the GPU resource contention among tasks, we analytically derive a multi-input–multi-output (MIMO) system model that captures the impacts of task rate adaptation on the response times of different tasks. Building on this model, we design a MIMO controller that dynamically adjusts task rates based on measured response times. Our extensive hardware testbed results on an NVIDIA RTX 3090 GPU and an AMD MI-100 GPU demonstrate that FC-GPU can provide better real-time performance even when the task execution times significantly increase at runtime.
@article{10.1145/3761812, author = {Subramaniyan, Srinivasan and Wang, Xiaorui}, title = {FC-GPU: Feedback Control GPU Scheduling for Real-Time Embedded Systems}, year = {2025}, issue_date = {September 2025}, publisher = {Association for Computing Machinery}, address = {New York, NY, USA}, volume = {24}, number = {5s}, issn = {1539-9087}, url = {https://doi.org/10.1145/3761812}, doi = {10.1145/3761812}, journal = {ACM Transactions on Embedded Computing Systems}, month = sep, articleno = {155}, numpages = {25}, keywords = {GPU scheduling, real-time systems, embedded systems, feedback control}, }