Design space exploration of FPGA-based Deep Convolutional Neural Networks

Deep Convolutional Neural Networks (DCNN) have proven to be very effective in many pattern recognition applications, such as image classification and speech recognition. Due to their computational complexity, DCNNs demand implementations that utilize custom hardware accelerators to meet performance...

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Bibliographic Details
Published in:Proceedings of the ASP-DAC ... Asia and South Pacific Design Automation Conference pp. 575 - 580
Main Authors: Motamedi, Mohammad, Gysel, Philipp, Akella, Venkatesh, Ghiasi, Soheil
Format: Conference Proceeding Journal Article
Language:English
Published: IEEE 01.01.2016
Subjects:
Accelerators
Computer architecture
Computer simulation
Convolution
Design analysis
Design engineering
Field programmable gate arrays
Kernel
Mathematical model
Mathematical models
Neural networks
Parallel processing
Platforms
Space exploration
ISSN:2153-697X
Online Access:Get full text
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Summary:Deep Convolutional Neural Networks (DCNN) have proven to be very effective in many pattern recognition applications, such as image classification and speech recognition. Due to their computational complexity, DCNNs demand implementations that utilize custom hardware accelerators to meet performance and energy-efficiency constraints. In this paper we propose an FPGA-based accelerator architecture which leverages all sources of parallelism in DCNNs. We develop analytical feasibility and performance estimation models that take into account various design and platform parameters. We also present a design space exploration algorithm for obtaining the implementation with the highest performance on a given platform. Simulation results with a real-life DCNN demonstrate that our accelerator outperforms other competing approaches, which disregard some sources of parallelism in the application. Most notably, our accelerator runs 1.9× faster than the state-of-the-art DCNN accelerator on the same FPGA device.
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ISSN:2153-697X
DOI:10.1109/ASPDAC.2016.7428073