.png){kind=icon}
.png){kind=icon}
Return
Throughput-Optimized FPGA Accelerator for Deep Convolutional Neural Networks
.png){kind=icon}
PRE
AI
DOI：10.1145/3079758.png)
.png){kind=icon}
Original request for help.png){kind=icon}
Share.png){kind=icon}
Save
Abstract
En 中文
Deep convolutional neural networks (CNNs) have gained great success in various computer vision applications. State-of-the-art CNN models for large-scale applications are computation intensive and memory expensive and, hence, are mainly processed on high-performance processors like server CPUs and GPUs. However, there is an increasing demand of high-accuracy or real-time object detection tasks in large-scale clusters or embedded systems, which requires energy-efficient accelerators because of the green computation requirement or the limited battery restriction. Due to the advantages of energy efficiency and reconfigurability, Field-Programmable Gate Arrays (FPGAs) have been widely explored as CNN accelerators. In this article, we present an in-depth analysis of computation complexity and the memory footprint of each CNN layer type. Then a scalable parallel framework is proposed that exploits four levels of parallelism in hardware acceleration. We further put forward a systematic design space exploration methodology to search for the optimal solution that maximizes accelerator throughput under the FPGA constraints such as on-chip memory, computational resources, external memory bandwidth, and clock frequency. Finally, we demonstrate the methodology by optimizing three representative CNNs (LeNet, AlexNet, and VGG-S) on a Xilinx VC709 board. The average performance of the three accelerators is 424.7, 445.6, and 473.4GOP/s under 100MHz working frequency, which outperforms the CPU and previous work significantly.
Keywords:
Design
Experimentation
Performance
FPGA architecture
convolutional neural networks
optimisation
high performance computing
application mapping
AI Summary
Key information extracted from the uploaded paper, including a brief overview, abstract, background, key highlights, visual analysis, and future outlook.
Journal
IF:
2.8
Papers:
597
Citations:
810