Rishov Sarkar
Orcid: 0000-0002-9168-0392
According to our database1,
Rishov Sarkar
authored at least 13 papers
between 2022 and 2025.
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Bibliography
2025
OmniSim: Simulating Hardware with C Speed and RTL Accuracy for High-Level Synthesis Designs.
CoRR, August, 2025
2024
HLSFactory: A Framework Empowering High-Level Synthesis Datasets for Machine Learning and Beyond.
Proceedings of the 2024 ACM/IEEE International Symposium on Machine Learning for CAD, 2024
LightningSimV2: Faster and Scalable Simulation for High-Level Synthesis via Graph Compilation and Optimization.
Proceedings of the 32nd IEEE Annual International Symposium on Field-Programmable Custom Computing Machines, 2024
Proceedings of the 32nd IEEE Annual International Symposium on Field-Programmable Custom Computing Machines, 2024
2023
Edge-MoE: Memory-Efficient Multi-Task Vision Transformer Architecture with Task-Level Sparsity via Mixture-of-Experts.
Proceedings of the IEEE/ACM International Conference on Computer Aided Design, 2023
INR-Arch: A Dataflow Architecture and Compiler for Arbitrary-Order Gradient Computations in Implicit Neural Representation Processing.
Proceedings of the IEEE/ACM International Conference on Computer Aided Design, 2023
FlowGNN: A Dataflow Architecture for Real-Time Workload-Agnostic Graph Neural Network Inference.
Proceedings of the IEEE International Symposium on High-Performance Computer Architecture, 2023
From Acceleration to Accelerating Acceleration: Modernizing the Accelerator Landscape using High-Level Synthesis.
Proceedings of the 31st IEEE Annual International Symposium on Field-Programmable Custom Computing Machines, 2023
Proceedings of the 31st IEEE Annual International Symposium on Field-Programmable Custom Computing Machines, 2023
2022
M<sup>3</sup>ViT: Mixture-of-Experts Vision Transformer for Efficient Multi-task Learning with Model-Accelerator Co-design.
CoRR, 2022
FlowGNN: A Dataflow Architecture for Universal Graph Neural Network Inference via Multi-Queue Streaming.
CoRR, 2022
M³ViT: Mixture-of-Experts Vision Transformer for Efficient Multi-task Learning with Model-Accelerator Co-design.
Proceedings of the Advances in Neural Information Processing Systems 35: Annual Conference on Neural Information Processing Systems 2022, 2022