LightningSimV2: Faster and Scalable Simulation for High-Level Synthesis via Graph Compilation and Optimization-Reference-Cited by-同舟云学术

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LightningSimV2: Faster and Scalable Simulation for High-Level Synthesis via Graph Compilation and Optimization

Published:2024-05-05 Issue: Volume:2 Page:104-114
ISSN:
Container-title:2024 IEEE 32nd Annual International Symposium on Field-Programmable Custom Computing Machines (FCCM)
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Author:

Sarkar Rishov¹,Paul Rachel¹,Hao Cong Callie¹

Affiliation:

1. School of Electrical and Computer Engineering, Georgia Institute of Technology

Publisher

IEEE

Link

http://xplorestaging.ieee.org/ielx8/10653616/10653625/10653673.pdf?arnumber=10653673

Reference14 articles.

1. LightningSim: Fast and Accurate Trace-Based Simulation for High-Level Synthesis

2. FlowGNN: A Dataflow Architecture for Real-Time Workload-Agnostic Graph Neural Network Inference

3. INR-Arch: A Dataflow Architecture and Compiler for Arbitrary-Order Gradient Computations in Implicit Neural Representation Processing

4. FastSim: A Fast Simulation Framework for High-Level Synthesis

5. FLASH: Fast, Parallel, and Accurate Simulator for HLS

Cited by 1 articles. 订阅此论文施引文献订阅此论文施引文献，注册后可以免费订阅5篇论文的施引文献，订阅后可以查看论文全部施引文献

1. 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

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