Generating multi-purpose accelerators using neural networks
Abstract/Contents
- Abstract
- Approximate computing (AC) is a very promising design paradigm for crossing the CPU power wall, primarily driven by the potential to sacrifice application output quality for significant gains in performance, energy, and fault tolerance. AC exploits the tolerance of many application domains (e.g., multimedia processing, data mining, and scientific computing) to errors and/or low-precision in their computations. However, existing solutions on the software side have not thoroughly explored the compilation and runtime stages, which have a critical impact on system performance. This work introduces a software-only, general-purpose acceleration framework that utilizes neural networks (NNs) and denoising autoencoders to restructure an application's data flow into a hybrid of exact and approximate computations. Specifically, this paper proposes advanced compilation and runtime techniques that solve the very difficult challenge of utilizing multiple interacting subtask approximators. Unlike previous work, our system exploits the hierarchical structure of an application to introduce significant flexibility in how the approximations are performed. Additionally, the framework is able to automatically generate an application.s subroutine structure in order to minimize design costs. By restructuring algorithms to have a mixed exact-NN data flow, EMEURO is able to achieve significant speedup across several domains, achieving 7x-109x maximum speedup over the original algorithm, with 0.1%-10% approximation error. Design costs are significantly reduced by allowing the NNs to learn the application.s functionality rather than being explicitly programmed and optimized by a human. NNs have also been shown to be very fault tolerant, which is particularly important in high-performance systems, where sophisticated designs can yield complex and hard to trace bugs. Although this work focuses on CPU-only acceleration, the very data-parallel nature of NNs makes them amenable to running efficiently on many different acceleration platforms.
Description
| Type of resource | text |
|---|---|
| Form | electronic; electronic resource; remote |
| Extent | 1 online resource. |
| Publication date | 2017 |
| Issuance | monographic |
| Language | English |
Creators/Contributors
| Associated with | McAfee, Lawrence | |
|---|---|---|
| Associated with | Stanford University, Department of Electrical Engineering. | |
| Primary advisor | Olukotun, Oyekunle Ayinde | |
| Thesis advisor | Olukotun, Oyekunle Ayinde | |
| Thesis advisor | Kozyrakis, Christoforos, 1974- | |
| Thesis advisor | Ng, Andrew Y, 1976- | |
| Advisor | Kozyrakis, Christoforos, 1974- | |
| Advisor | Ng, Andrew Y, 1976- |
Subjects
| Genre | Theses |
|---|
Bibliographic information
| Statement of responsibility | Lawrence McAfee. |
|---|---|
| Note | Submitted to the Department of Electrical Engineering. |
| Thesis | Thesis (Ph.D.)--Stanford University, 2017. |
| Location | electronic resource |
Access conditions
- Copyright
- © 2017 by Lawrence Christopher McAfee
- License
- This work is licensed under a Creative Commons Attribution Non Commercial 3.0 Unported license (CC BY-NC).
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