Current Search: Lanham, Grant Jr (x)
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Title
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NEURALSYNTH - A NEURAL NETWORK TO FPGA COMPILATION FRAMEWORK FOR RUNTIME EVALUATION.
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Creator
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Lanham, Grant Jr, Hallstrom, Jason O., Florida Atlantic University, Department of Computer and Electrical Engineering and Computer Science, College of Engineering and Computer Science
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Abstract/Description
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Artificial neural networks are increasing in power, with attendant increases in demand for efficient processing. Performance is limited by clock speed and degree of parallelization available through multi-core processors and GPUs. With a design tailored to a specific network, a field-programmable gate array (FPGA) can be used to minimize latency without the need for geographically distributed computing. However, the task of programming an FPGA is outside the realm of most data scientists....
Show moreArtificial neural networks are increasing in power, with attendant increases in demand for efficient processing. Performance is limited by clock speed and degree of parallelization available through multi-core processors and GPUs. With a design tailored to a specific network, a field-programmable gate array (FPGA) can be used to minimize latency without the need for geographically distributed computing. However, the task of programming an FPGA is outside the realm of most data scientists. There are tools to program FPGAs from a high level description of a network, but there is no unified interface for programmers across these tools. In this thesis, I present the design and implementation of NeuralSynth, a prototype Python framework which aims to bridge the gap between data scientists and FPGA programming for neural networks. My method relies on creating an extensible Python framework that is used to automate programming and interaction with an FPGA. The implementation includes a digital design for the FPGA that is completed by a Python framework. Programming and interacting with the FPGA does not require leaving the Python environment. The extensible approach allows multiple implementations, resulting in a similar workflow for each implementation. For evaluation, I compare the results of my implementation with a known neural network framework.
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Date Issued
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2020
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PURL
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http://purl.flvc.org/fau/fd/FA00013533
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Subject Headings
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Artificial neural networks, Neural networks (Computer science)--Design, Field programmable gate arrays, Python (Computer program language)
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Format
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Document (PDF)
