Neelesh
Gupta.

About

I am a second-year Ph.D. student in Computer Engineering at USC, working in the FPGA/Parallel Computing Lab, advised by Prof. Viktor K. Prasanna and Prof. Raj Kannan.

Research interests

• Machine learning for systems
• Graph machine learning
• Agentic and physical AI
• Subquadratic neural network architectures
• Heterogeneous computing and hardware/software co-design

Teaching

• Fall 2026: Lab Teaching Assistant, EE 354L: Introduction to Digital Circuits, USC.

Service

• HiPC 2025: Publicity Chair, IEEE International Conference on High Performance Computing, Data, and Analytics.

Publications

1. HPEC ‘26
   Topology Survives Shuffling: Robust Long-Context Memory Access Prediction for Graph Analytics
   Dongyan Sun, Neelesh Gupta, Rajgopal Kannan, Viktor Prasanna
   IEEE High Performance Extreme Computing Conference (HPEC 2026), forthcoming.
   Outstanding Student Paper
   

2. JPDC
   Net2Tab: Tabularizing Neural Networks with Applications to Data Prefetching
   Pengmiao Zhang, Neelesh Gupta, Rajgopal Kannan, Viktor K. Prasanna
   Journal of Parallel and Distributed Computing, vol. 213, article 105265, July 2026.
   Paper

3. IPDPS RAW ‘26
   A Persistent-State Dataflow Accelerator for Memory-Bound Linear Attention Decode on FPGA
   Neelesh Gupta, Peter Wang, Rajgopal Kannan, Viktor K. Prasanna
   Reconfigurable Architectures Workshop (RAW 2026), held with IEEE IPDPS, poster.
   Best Poster Award
   arXiv

4. HiPC ‘25
   Context-Driven Performance Modeling for Causal Inference Operators on Neural Processing Units
   Neelesh Gupta, Rakshith Jayanth, Dhruv Parikh, Viktor Prasanna
   32nd IEEE International Conference on High Performance Computing, Data, and Analytics (HiPC 2025), pp. 365–375.
   PDF

5. HiPCW ‘25
   Enabling Long FFT Convolutions on Memory-Constrained FPGAs via Chunking
   Peter Wang, Neelesh Gupta, Viktor K. Prasanna
   32nd IEEE International Conference on High Performance Computing, Data and Analytics Workshop (HiPCW 2025), pp. 321–322.
   PDF Paper

6. HPEC ‘25
   Performance-Energy Characterization of ML Inference on Heterogeneous Edge AI Platforms
   Palash Kohli, Rakshith Jayanth, Neelesh Gupta, Haoyang Fan, Viktor Prasanna
   IEEE High Performance Extreme Computing Conference (HPEC 2025), pp. 1–7.
   Paper

7. HiPCW ‘24
   Towards Real-Time LLM Inference on Heterogeneous Edge Platforms
   Rakshith Jayanth, Neelesh Gupta, Souvik Kundu, Deepak A. Mathaikutty, Viktor Prasanna
   31st IEEE International Conference on High Performance Computing, Data and Analytics Workshop (HiPCW 2024), pp. 197–198.
   PDF Paper

8. HPEC ‘24
   Benchmarking Edge AI Platforms for High-Performance ML Inference
   Rakshith Jayanth, Neelesh Gupta, Viktor K. Prasanna
   IEEE High Performance Extreme Computing Conference (HPEC 2024).
   PDF

9. CF ‘24
   TabConv: Low-Computation CNN Inference via Table Lookups
   Neelesh Gupta, Narayanan Kannan, Pengmiao Zhang, Viktor K. Prasanna
   Proceedings of the 21st ACM International Conference on Computing Frontiers (CF 2024).
   PDF Code

10. IPDPS ‘24
    Attention, Distillation, and Tabularization: Towards Practical Neural Network-Based Prefetching
    Pengmiao Zhang, Neelesh Gupta, Rajgopal Kannan, Viktor K. Prasanna
    38th IEEE International Parallel & Distributed Processing Symposium (IPDPS 2024).
    PDF Code

11. HPEC ‘23
    PaCKD: Pattern-Clustered Knowledge Distillation for Compressing Memory Access Prediction Models
    Neelesh Gupta, Pengmiao Zhang, Rajgopal Kannan, Viktor K. Prasanna
    IEEE High Performance Extreme Computing Conference (HPEC 2023).
    PDF Code

12. KDD-UC ‘22
    Did your child get disturbed by an inappropriate advertisement on YouTube?
    Jeffrey Liu, Rajat Tandon, Uma Durairaj, Jiani Guo, Spencer Zahabizadeh, Sanjana Ilango, Jeremy Tang, Neelesh Gupta, Zoe Zhou, Jelena Mirkovic
    Proceedings of the KDD Undergraduate Consortium (KDD-UC 2022).
    PDF

Last updated September 7, 2026