TFApprox: Towards a Fast Emulation of DNN Approximate Hardware Accelerators on GPU

Result code in IS VaVaI

<a href="https://www.isvavai.cz/riv?ss=detail&h=RIV%2F00216305%3A26230%2F20%3APU138605" target="_blank" >RIV/00216305:26230/20:PU138605 - isvavai.cz</a>

Result on the web

<a href="https://www.fit.vut.cz/research/publication/12072/" target="_blank" >https://www.fit.vut.cz/research/publication/12072/</a>

DOI - Digital Object Identifier

<a href="http://dx.doi.org/10.23919/DATE48585.2020.9116299" target="_blank" >10.23919/DATE48585.2020.9116299</a>

Result language

angličtina

Original language name

TFApprox: Towards a Fast Emulation of DNN Approximate Hardware Accelerators on GPU

Original language description

Energy efficiency of hardware accelerators of deep neural networks (DNN) can be improved by introducing approximate arithmetic circuits. In order to quantify the error introduced by using these circuits and avoid the expensive hardware prototyping, a software emulator of the DNN accelerator is usually executed on CPU or GPU. However, this emulation is typically two or three orders of magnitude slower than a software DNN implementation running on CPU or GPU and operating with standard floating point arithmetic instructions and common DNN libraries. The reason is that there is no hardware support for approximate arithmetic operations on common CPUs and GPUs and these operations have to be expensively emulated. In order to address this issue, we propose an efficient emulation method for approximate circuits utilized in a given DNN accelerator which is emulated on GPU. All relevant approximate circuits are implemented as look-up tables and accessed through a texture memory mechanism of CUDA capable GPUs. We exploit the fact that the texture memory is optimized for irregular read-only access and in some GPU architectures is even implemented as a dedicated cache. This technique allowed us to reduce the inference time of the emulated DNN accelerator approximately 200 times with respect to an optimized CPU version on complex DNNs such as ResNet. The proposed approach extends the TensorFlow library and is available online at https://github.com/ehw-fit/tf-approximate.

Czech name

—

Czech description

—

Type

D - Article in proceedings

CEP classification

—

OECD FORD branch

10201 - Computer sciences, information science, bioinformathics (hardware development to be 2.2, social aspect to be 5.8)

Project

<a href="/en/project/GA19-10137S" target="_blank" >GA19-10137S: Designing and exploiting libraries of approximate circuits</a><br>

Continuities

P - Projekt vyzkumu a vyvoje financovany z verejnych zdroju (s odkazem do CEP)

Publication year

2020

Confidentiality

S - Úplné a pravdivé údaje o projektu nepodléhají ochraně podle zvláštních právních předpisů

Article name in the collection

2020 Design, Automation & Test in Europe Conference & Exhibition (DATE)

ISBN

978-3-9819263-4-7

ISSN

—

e-ISSN

—

Number of pages

4

Pages from-to

294-297

Publisher name

Institute of Electrical and Electronics Engineers

Place of publication

Grenoble

Event location

Grenoble

Event date

Mar 9, 2020

Type of event by nationality

WRD - Celosvětová akce

UT code for WoS article

000610549200053