Approximation of CPU code using neural networks

Gardner, Conor S

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https://hdl.handle.net/2142/97481 Copy

Description

Title

Approximation of CPU code using neural networks

Author(s)

Gardner, Conor S

Issue Date

2017-04-26

Director of Research (if dissertation) or Advisor (if thesis)

Kim, Nam S

Department of Study

Electrical & Computer Eng

Discipline

Electrical & Computer Engr

Degree Granting Institution

University of Illinois at Urbana-Champaign

Degree Name

M.S.

Degree Level

Thesis

Keyword(s)

• Neural
• Network
• Code
• Approximation
• Training
• Program
• Trace
• Convolution

Abstract

There is a well-known spectrum of computing hardware ranging from central processing units (CPUs) to highly specialized application specific integrated circuits (ASICs). Most consumer CPUs are general purpose and come with mature development tools used by large communities of programmers, while ASICs can perform very specific tasks very efficiently at the expense of ease-of-use and flexibility. Other devices such as digital signal processors (DSPs), graphics processing units (GPUs), and field programmable gate arrays (FPGAs) occupy intermediate interpolations on the usability-efficiency continuum. New development tools such as very long instruction word (VLIW) compilers, CUDA, and logic synthesis have made it easier than ever for even novice programmers to leverage the increased efficiency of DSP cores, GPUs, and FPGAs using specialized high-level programming languages for those devices. However, even after surmounting the steep learning curve, a skilled programmer will still require significantly more time to write and validate a CUDA or OpenCL function compared to an equivalent CPU function. Neural nets are fairly general purpose tools which can perform pattern recognition or arithmetic operations on a block of input data and produce a corresponding block of output data. The aim of this project is to be able to select a fairly arbitrary block of code such as a C++ function and train a neural net to mimic the original code's input-output behavior. Once the neural net has been trained, it can run on a highly parallel device such as a GPU without the programmer ever needing to write a CUDA program. Of course, this approach also has inherent drawbacks. First, all dependent processing which consumes output data from the neural net must be able to tolerate errors, since the network can only approximate the original code. Second, since neural nets require many, often unnecessary, floating point operations, there will be a large amount of “bloat” in the neural implementation which must be offset by the benefits gained by running the workload on a highly parallel device to be practical.

Graduation Semester

2017-05

Type of Resource

text

Permalink

http://hdl.handle.net/2142/97481

Copyright and License Information

Copyright 2017 Conor Gardner

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