

Sasindu Wijeratne

I am a Ph.D. candidate in the Department of Electrical and Computer Engineering, University of Southern California. I'm working with Professor Viktor Prasanna in FPGA/Parallel Computing Lab. My research interests include developing parallel algorithms and custom hardware designs for sparse algebraic applications, including tensor decomposition and scalable static/dynamic graph processing. My current research focuses on tensor decomposition on heterogeneous systems.​​
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Find my current CV here.
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Doctor of Philosophy in Electrical and Computer Engineering, 2019 - Present
​ University of Southern California, Los Angeles, USA
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Master of Science in Electrical and Computer Engineering, 2019 - 2021
​ University of Southern California, Los Angeles, USA
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B.Sc in Electronics and Telecommunication Engineering, 2014 - 2018
University of Moratuwa, Sri Lanka
Recent News!
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April 30, 2020 - It was a pleasure to teach CSCI 356: Introduction to Computer Systems with Dr. Marco Paolieri for the 2nd time!
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April 30, 2020 - Done with my teaching tasks for EE 599: Accelerated Computing Using Field Programmable Gate Arrays with professor Viktor Prasanna
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June 01, 2021 - Volunteering as a Organizing Committee member for HiPC 2021
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January 10, 2022 - Volunteering as a Organizing Committee member for HiPC 2022
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August 13, 2022 - Volunteering as a Organizing Committee member for FCCM 2023
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Jan 10, 2023 - Started teaching EE 354L: Introduction to Digital Circuits with professor Gandhi Puvvada for the 6th time
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Jan 31, 2023 - PhD Candidate now
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Feb 1, 2023: My recent paper will be appear in FPGA 2023!
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Feb 5, 2023: Received the ACM travel grant to attend FPGA 2023. See you at Monterey!
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Sept 27, 2023: Selected as an USC MHI Ph.D. Scholar Finalist
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Sept 30, 2023: My paper, Dynasor, is selected as a best paper candidate at 35th IEEE International Symposium on Computer Architecture and High Performance Computing
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Nov 30, 2023: Attended DARPA NaPSAC Kick-off Meeting in DC
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Feb 15, 2024: My paper on accelerating MTTKRP on GPUs is accepted as a full paper in CF' 24
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July 10, 2024: Current powerlifting status (Total: 1065 LB): 475 LB - Deadlift, 365 LB - Squat, and 225 LB - Bench; (at 180 LB)
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Sept. 18, 2024: My paper on Accelerating Sparse MTTKRP for Small Tensor Decomposition on GPU is selected as a best paper candidate at Asilomar Conference on Signals, Systems, and Computers
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Sept. 24, 2024: My paper on Predictive Performance of Photonic SRAM-based In-Memory Computing for Tensor Decomposition is selected as the best student paper at HPEC 2024
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Nov, 8, 2024: My poster on Taming Sparse Tensor Decomposition: Unified Acceleration Framework received Outstanding Poster Award (Parallel and Distributed Computing track) at 14th Annual Research Festival organized by USC Ming Hsieh Department of Electrical Engineering
Publications:
Thesis Work:
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Wijeratne, S., Kannan, R., Prasanna, V. (2024). DynaRemap: Hardware Agnostic Parallel Algorithm with Dynamic Tensor Remapping to Accelerate MTTKRP for Sparse Tensor Decomposition. In IEEE Transactions on Parallel and Distributed Systems. [In preparation]
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Wijeratne, S., Kannan, R., Prasanna, V. (2024). Dynasor-GPU: Dynamic Memory Layout for Acceler- ating sparse MTTKRP for Tensor Decomposition on GPU. In ACM Transactions on Parallel Computing. [In preparation]
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Wijeratne, S., Kannan, R., Prasanna, V. (2024). Accelerating MTTKRP for Sparse Tensor Decom- position on Multi-GPU Platform. In Proceedings of the 31st High Performance Computing, Data, and Analytics. [Submitted]
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Wijeratne, S., Kannan, R., Prasanna, V. (2024). Accelerating Sparse MTTKRP for Small Tensor Decomposition on GPU. In Proceedings of the 2024 Asilomar Conference on Signals, Systems, and Computers. [Best Paper Candidate]
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Wijeratne, S., Kannan, R., Prasanna, V. (2024). Sparse MTTKRP Acceleration for Tensor Decomposition on GPU. In Proceedings of the 21st ACM International Conference on Computing Frontiers.
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Wijeratne, S., Wang, T. Y., Kannan, R., Prasanna, V. (2023). Accelerating Sparse MTTKRP for Tensor Decomposition on FPGA. In Proceedings of the 2023 ACM/SIGDA International Symposium on Field Programmable Gate Arrays.
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Wijeratne, S., Kannan, R., Prasanna, V. (2023). Dynasor: A Dynamic Memory Layout for Accelerating Sparse MTTKRP for Tensor Decomposition on Multi-core CPU. In 35th IEEE International Symposium on Computer Architecture and High Performance Computing. [Best Paper Candidate]
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Wijeratne, S., Wang, T. Y., Kannan, R., Viktor, P. (2022). Towards Programmable Memory Controller for Tensor Decomposition. In 11th International Conference on Data Science, Technology and Applications (DATA’22).
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Other:
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Wijeratne, S., Sundar, S., Kaiser, Md., Jaiswal, A., Mathew, C., Jacob, A. P., Prasanna, V. (2024). Predictive Performance of Photonic SRAM-based In-Memory Computing for Tensor Decomposition. In 2024 IEEE High Performance Extreme Computing Conference (HPEC). [Best Student Paper]
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Wijeratne, S., Jaiswal, A., Jacob, A. P., Zhang, B., Prasanna, V. (2022). Performance modeling sparse MTTKRP using optical static random access memory on FPGA. In 2022 IEEE High Performance Extreme Computing Conference (HPEC).
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Wijeratne, S., Zhang, B., Kannan, R., Prasanna, V., Busart, C. (2023). PAHD: Perception-Action based Human Decision Making using Explainable Graph Neural Networks on SAR images. In Automatic Target Recognition XXXIII.
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Wijeratne, S., Pattnaik, S., Chen, Z., Kannan, R., Prasanna, V. (2021). Programmable fpga-based memory controller. In 2021 IEEE Symposium on High-Performance Interconnects (HOTI).
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Wijeratne, S., Kannan, R., Prasanna, V. (2021). Reconfigurable Low-latency Memory System for Sparse Matricized Tensor Times Khatri-Rao Product on FPGA. In 2021 IEEE High Performance Extreme Computing Conference (HPEC).
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Wijeratne, S., Ekanayake, A., Jayaweera, S., Ravishan, D., Pasqual, A. (2019). Scalable High Perfor- mance SDN Switch Architecture on FPGA for Core Networks. In Proceedings of the 2019 ACM/SIGDA International Symposium on Field-Programmable Gate Arrays.
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Wijeratne, S., Jayaweera, S., Dananjaya, M., Pasqual, A. (2018). Reconfigurable co-processor archi- tecture with limited numerical precision to accelerate deep convolutional neural networks. In 2018 IEEE 29Th international conference on application-specific systems, architectures and processors (ASAP).
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Chen, P., Manjunath, P., Wijeratne, S., Zhang, B., Prasanna, V. (2023). Exploiting On-chip Het- erogeneity of Versal Architecture for GNN Inference Acceleration. In 33rd International Conference on Field-Programmable Logic and Applications.
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Zhang, B., Wijeratne, S., Kannan, R., Prasanna, V., Busart, C. (2023). Graph neural network based SAR automatic target recognition with human-in-the-loop. In Algorithms for Synthetic Aperture Radar Imagery XXX.
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Zhang, B., Wijeratne, S., Kannan, R., Prasanna, V., Busart, C. (2023). Graph Neural Network for Accurate and Lowcomplexity SAR ATR. In 15th International Conference on Advanced Geographic Information Systems, Applications, and Services.
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Zhang, B., Wijeratne, S., Kannan, R., Prasanna, V., Busart, C. (2023). How can Human-in-the-loop Improve the Performance of SAR ATR? A Reinforcement Learning Based Approach. In International Radar Conference 2023. [Best Paper Candidate]
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Zhang, B., Jaiswal, A., Mathew, C., Lakkireddy, R., Jacob, A., Wijeratne, S., Prasanna, V. (2022). A high throughput parallel hash table on fpga using xor-based memory. In 2020 IEEE High performance extreme computing conference (HPEC).
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Ye, T., Kuppannagari, S. R., De Rose, C. A., Wijeratne, S., Kannan, R., Prasanna, V. (2022). Estimat- ing the Impact of Communication Schemes for Distributed Graph Processing. In 2022 21st International Symposium on Parallel and Distributed Computing (ISPDC).
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Zhang, R., Wijeratne, S., Yang, Y., Kuppannagari, S. R., Prasanna, V. (2020). A high throughput parallel hash table on fpga using xor-based memory. In 2020 IEEE High performance extreme computing conference (HPEC).
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Senanayake, R., Liyanage, N., Wijeratne, S., Atapattu, S., Ekanayake, A., Pasqual, A. (2017). High performance hardware architectures for Intra Block Copy and Palette Coding for HEVC screen content coding extension. In 2018 IEEE international conference on application-specific systems, architectures and processors (ASAP).