GNNHLS: Evaluating Graph Neural Network Inference via High-Level Synthesis

被引：2

作者：

Zhao, Chenfeng ^{[1
]}

Dong, Zehao ^{[1
]}

Chen, Yixin ^{[1
]}

Zhang, Xuan ^{[1
]}

Chamberlain, Roger D. ^{[1
]}

机构：

[1] Washington Univ, McKelvey Sch Engn, St Louis, MO 63110 USA

来源：

2023 IEEE 41ST INTERNATIONAL CONFERENCE ON COMPUTER DESIGN, ICCD | 2023年

关键词：

field-programmable gate arrays; graph neural networks; high-level synthesis;

D O I：

10.1109/ICCD58817.2023.00092

中图分类号：

TP3 [计算技术、计算机技术];

学科分类号：

0812 ;

摘要：

We present GNNHLS, an open-source framework to comprehensively evaluate GNN inference acceleration on FPGAs via HLS, containing a software stack for data generation and baseline deployment and FPGA implementations of 6 well-tuned GNN HLS kernels. Evaluating on 4 graph datasets with distinct topologies and scales, the results show that GNNHLS achieves up to 50.8x speedup and 423x energy reduction relative to the CPU baselines. Compared with the GPU baselines, GNNHLS achieves up to 5.16x speedup and 74.5x energy reduction.

引用

页码：574 / 577

页数：4

共 50 条

[21] Support for High-Level Quantum Bayesian Inference
Przewiezlikowski, Marcin
Grabowski, Michal
Kurzyk, Dariusz
Rycerz, Katarzyna
COMPUTATIONAL SCIENCE - ICCS 2019, PT V, 2019, 11540 : 764 - 770
[22] Capturing High-Level Semantic Correlations via Graph for Multimodal Sentiment Analysis
Qian, Fan
Han, Jiqing
Guan, Yadong
Song, Wenjie
He, Yongjun
IEEE SIGNAL PROCESSING LETTERS, 2024, 31 : 561 - 565
[23] A neural network for high-level cognitive control of serial order behavior
Donaldson, S
PROCEEDINGS OF THE 7TH JOINT CONFERENCE ON INFORMATION SCIENCES, 2003, : 1516 - 1519
[24] High-level Modeling of Manufacturing Faults in Deep Neural Network Accelerators
Kundu, Shamik
Soyyigit, Ahmet
Hoque, Khaza Anuarul
Basu, Kanad
2020 26TH IEEE INTERNATIONAL SYMPOSIUM ON ON-LINE TESTING AND ROBUST SYSTEM DESIGN (IOLTS 2020), 2020,
[25] Neural network macromodel for high-level power estimation of CMOS circuits
Qiang, W
Cao, Y
Yan, YY
Gao, X
PROCEEDINGS OF THE 2005 INTERNATIONAL CONFERENCE ON NEURAL NETWORKS AND BRAIN, VOLS 1-3, 2005, : 1009 - 1014
[26] Learning a Smart Convolutional Neural Network with High-level Semantic Information
Qiao, Xinshu
Xu, Chunyan
Yang, Jian
Jiang, Jiatao
PROCEEDINGS 2017 4TH IAPR ASIAN CONFERENCE ON PATTERN RECOGNITION (ACPR), 2017, : 190 - 195
[27] High-Level Programming via Generalized Planning and LTL Synthesis
Bonet, Blai
De Giacomo, Giuseppe
Geffner, Hector
Patrizi, Fabio
Rubin, Sasha
KR2020: PROCEEDINGS OF THE 17TH INTERNATIONAL CONFERENCE ON PRINCIPLES OF KNOWLEDGE REPRESENTATION AND REASONING, 2020, : 152 - 161
[28] Predicting neural network confidence using high-level feature distance
Wang, Jie
Ai, Jun
Lu, Minyan
Liu, Jingyu
Wu, Zili
INFORMATION AND SOFTWARE TECHNOLOGY, 2023, 159
[29] Learning spatial hierarchies of high-level features in deep neural network
Razzaghi, Parvin
Abbasi, Karim
Bayat, Pegah
JOURNAL OF VISUAL COMMUNICATION AND IMAGE REPRESENTATION, 2020, 70
[30] High-level synthesis based upon dependence graph for multi-FPGA
Akil, M
INFORMACIJE MIDEM-JOURNAL OF MICROELECTRONICS ELECTRONIC COMPONENTS AND MATERIALS, 2003, 33 (04): : 267 - 275

← 1 2 3 4 5 →