Scalable and modularized RTL compilation of Convolutional Neural Networks onto FPGA

Despite its popularity, deploying Convolutional Neural Networks (CNNs) on a portable system is still challenging due to large data volume, intensive computation and frequent memory access. Although previous FPGA acceleration schemes generated by high-level synthesis tools (i.e., HLS, OpenCL) have al...

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Bibliographic Details
Published in:International Conference on Field-programmable Logic and Applications pp. 1 - 8
Main Authors: Yufei Ma, Suda, Naveen, Yu Cao, Jae-sun Seo, Vrudhula, Sarma
Format: Conference Proceeding
Language:English
Published: EPFL 01.08.2016
Subjects:
Acceleration
Algorithm design and analysis
Convolution
Convolutional neural networks
Field programmable gate arrays
FPGA
Hardware
hardware acceleration
Kernel
Memory management
ISSN:1946-1488
Online Access:Get full text
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Summary:Despite its popularity, deploying Convolutional Neural Networks (CNNs) on a portable system is still challenging due to large data volume, intensive computation and frequent memory access. Although previous FPGA acceleration schemes generated by high-level synthesis tools (i.e., HLS, OpenCL) have allowed for fast design optimization, hardware inefficiency still exists when allocating FPGA resources to maximize parallelism and throughput. A direct hardware-level design (i.e., RTL) can improve the efficiency and achieve greater acceleration. However, this requires an in-depth understanding of both the algorithm structure and the FPGA system architecture. In this work, we present a scalable solution that integrates the flexibility of high-level synthesis and the finer level optimization of an RTL implementation. The cornerstone is a compiler that analyzes the CNN structure and parameters, and automatically generates a set of modular and scalable computing primitives that can accelerate various deep learning algorithms. Integrating these modules together for end-to-end CNN implementations, this work quantitatively analyzes the complier's design strategy to optimize the throughput of a given CNN model with the FPGA resource constraints. The proposed methodology is demonstrated on Altera Stratix-V GXA7 FPGA for AlexNet and NIN CNN models, achieving 114.5 GOPS and 117.3 GOPS, respectively. This represents a 1.9× improvement in throughput when compared to the OpenCL-based design. The results illustrate the promise of the automatic compiler solution for modularized and scalable hardware acceleration of deep learning.
ISSN:1946-1488
DOI:10.1109/FPL.2016.7577356