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Neuraghe: Exploiting CPU-FPGA synergies for efficient and flexible CNN inference acceleration on zynQ SoCs
- Publication Year :
- 2018
-
Abstract
- Deep convolutional neural networks (CNNs) obtain outstanding results in tasks that require human-level understanding of data, like image or speech recognition. However, their computational load is significant, motivating the development of CNN-specialized accelerators. This work presents NEURAghe, a flexible and efficient hardware/software solution for the acceleration of CNNs on Zynq SoCs. NEURAghe leverages the synergistic usage of Zynq ARM cores and of a powerful and flexible Convolution-Specific Processor deployed on the reconfigurable logic. The Convolution-Specific Processor embeds both a convolution engine and a programmable soft core, releasing the ARM processors from most of the supervision duties and allowing the accelerator to be controlled by software at an ultra-fine granularity. This methodology opens the way for cooperative heterogeneous computing: while the accelerator takes care of the bulk of the CNN workload, the ARM cores can seamlessly execute hard-to-accelerate parts of the computational graph, taking advantage of the NEON vector engines to further speed up computation. Through the companion NeuDNN SW stack, NEURAghe supports end-to-end CNN-based classification with a peak performance of 169 Gops/s, and an energy efficiency of 17 Gops/W. Thanks to our heterogeneous computing model, our platform improves upon the state-of-the-art, achieving a frame rate of 5.5 fps on the end-to-end execution of VGG-16, and 6.6 fps on ResNet-18.<br />22 pages, 14 figures, submitted to ACM Transactions on Reconfigurable Technology and Systems
- Subjects :
- FOS: Computer and information sciences
Speedup
General Computer Science
Computer science
Image classification
Convolutional neural network
Symmetric multiprocessor system
02 engineering and technology
Software
Hardware Architecture (cs.AR)
0202 electrical engineering, electronic engineering, information engineering
Neural and Evolutionary Computing (cs.NE)
HW accelerator
Field-programmable gate array
Computer Science - Hardware Architecture
business.industry
Convolutional neural networks
FPGAS
Computer Science (all)
020208 electrical & electronic engineering
Computer Science - Neural and Evolutionary Computing
Frame rate
020202 computer hardware & architecture
ARM architecture
Computer Science - Distributed, Parallel, and Cluster Computing
Embedded system
Graph (abstract data type)
Distributed, Parallel, and Cluster Computing (cs.DC)
business
Subjects
Details
- Language :
- English
- Database :
- OpenAIRE
- Accession number :
- edsair.doi.dedup.....c2471fd182d8794ea73ae11f7f652555