LightningSim: Fast and Accurate Trace-Based Simulation for High-Level Synthesis
Author:
Affiliation:
1. School of Electrical and Computer Engineering, Georgia Institute of Technology
Funder
Georgia Tech
Publisher
IEEE
Link
http://xplorestaging.ieee.org/ielx7/10171436/10171441/10171498.pdf?arnumber=10171498
Reference12 articles.
1. FlowGNN: A Dataflow Architecture for Real-Time Workload-Agnostic Graph Neural Network Inference
2. I RON M AN
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1. 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;2024-09-09
2. Ph.D. Project: Modernizing High-Level Hardware Design Workflows;2024 IEEE 32nd Annual International Symposium on Field-Programmable Custom Computing Machines (FCCM);2024-05-05
3. LightningSimV2: Faster and Scalable Simulation for High-Level Synthesis via Graph Compilation and Optimization;2024 IEEE 32nd Annual International Symposium on Field-Programmable Custom Computing Machines (FCCM);2024-05-05
4. HLPerf: Demystifying the Performance of HLS-based Graph Neural Networks with Dataflow Architectures;ACM Transactions on Reconfigurable Technology and Systems;2024-04-02
5. INR-Arch: A Dataflow Architecture and Compiler for Arbitrary-Order Gradient Computations in Implicit Neural Representation Processing;2023 IEEE/ACM International Conference on Computer Aided Design (ICCAD);2023-10-28
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