Heterogeneous FPGA-Based Cost-Optimal Design for Timing-Constrained CNNs

Field programmable gate array (FPGA) has been one of the most popular platforms to implement convolutional neural networks (CNNs) due to its high performance and cost efficiency; however, limited by the on-chip resources, the existing single-FPGA architectures cannot fully exploit the parallelism in...

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Bibliographic Details
Published in:IEEE transactions on computer-aided design of integrated circuits and systems Vol. 37; no. 11; pp. 2542 - 2554
Main Authors: Jiang, Weiwen, Sha, Edwin Hsing-Mean, Zhuge, Qingfeng, Yang, Lei, Chen, Xianzhang, Hu, Jingtong
Format: Journal Article
Language:English
Published: New York IEEE 01.11.2018
The Institute of Electrical and Electronics Engineers, Inc. (IEEE)
Subjects:
Algorithms
Artificial neural networks
Buffers
Convolutional neural networks
Convolutional neural networks (CNNs)
Dynamic programming
Embedded systems
Field programmable gate arrays
Hetero-nanocrystal memory
heterogeneous field programmable gate array (FPGA) cluster
Integer programming
Linear programming
Memory management
Minimum cost
Mixed integer
Optimization
optimization algorithms
Parallel processing
partitioning and mapping
Pipelines
Task analysis
Timing
ISSN:0278-0070, 1937-4151
Online Access:Get full text
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Summary:Field programmable gate array (FPGA) has been one of the most popular platforms to implement convolutional neural networks (CNNs) due to its high performance and cost efficiency; however, limited by the on-chip resources, the existing single-FPGA architectures cannot fully exploit the parallelism in CNNs. In this paper, we explore heterogeneous FPGA-based designs to effectively leverage both task and data parallelism, such that the resultant system can achieve the minimum cost while satisfying timing constraints. In order to maximize the task parallelism, we investigate two critical problems: 1) buffer placement , where to place buffers to partition CNNs into pipeline stages and 2) task assignment , what type of FPGA to implement different CNN layers. We first formulate the system-level optimization problem with a mixed integer linear programming model. Then, we propose an efficient dynamic programming algorithm to obtain the optimal solutions. On top of that, we devise an efficient algorithm that exploits data parallelism within CNN layers to further improve cost efficiency. Evaluations on well-known CNNs demonstrate that the proposed techniques can obtain an average of 30.82% reduction in system cost under the same timing constraint, and an average of 1.5 times speedup in performance under the same cost budget, compared with the state-of-the-art techniques.
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ISSN:0278-0070
1937-4151
DOI:10.1109/TCAD.2018.2857098