Hartung-Gorre Verlag
Inh.: Dr.
Renate Gorre D-78465
Konstanz Fon: +49 (0)7533 97227 Fax: +49 (0)7533 97228 www.hartung-gorre.de
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Series in
Microelectronics
edited by
Qiuting
Huang
Andreas Schenk
Mathieu
Maurice Luisier
Bernd
Witzigmann
Florian Michael Scheidegger
The concept of
transprecision computing
2020. XXII, 232 pages. € 64,00.
ISBN 978-3-86628-689-4
Abstract:
For many years, computing systems rely on guaranteed
numerical
precision of each step in complex computations. Moore’s law sustains
exponential improvements in the semiconductor industry over
several
decades for building computing infrastructure, from tiny
Internet-of-
Things nodes, over personal smartphones, laptops or
workstations, up
to large high performance computing (HPC) computing
server centers.
With the paradigm of the ”power
wall”, achievable improvements
start to saturate. To that end, the concept of transprecision computing
emerged, where existing over-conservative ”precis” computing
assumptions are relaxed and replaced with more flexible and
efficient
policies to gain performance.
Unfortunately, it is non-straight forward to adopt and
integrate
general transprecision concepts
into the variety of today’s computing
infrastructure. The main challenge consists of leveraging domainspecific
knowledge and provide full solutions covering from physical
foundations over circuit-level up through the full software stack
to the
application level.
This work focuses on how transprecision
concepts improve general
computing. We identify and elaborate the standard number
representations,
especially the one defined in the IEEE 754 floating-point
standard, as the enabler of low precision computing. We developed
lightweight libraries that allow integrating transprecision
concepts
into algorithms. Finally, we focus on building automatized
workflows
for specific problems, where the solution space is
enlarged by multiple
orders of magnitude due to the various configurations of low
precision.
We demonstrate how heuristic optimization strategies
applied on top
of transprecision computing
find near to optimal configurations of
approximated kernels in a short time.
Keywords: Transprecision
computing, approximation computing, floatx, reduced
precision, energy efficient, reduzierte Genauigkeit, Energieeffizienz
About the Author:
Florian Scheidegger was born
in Bern, Switzerland, in 1990. He
received his M.Sc. degree in electrical engineering and
information
technology from ETH Zurich, Switzerland, in 2016. In the
same year, he worked for five months at the Integrated
Systems
Laboratory, ETH Zurich, developing a spatio-temporal video filtering
pipeline and a neural net based video classifier. In January
2017, he started his doctoral studies at IBM research
Zurich pursuing
a Ph.D. degree in collaboration with the Integrated
Systems
Laboratory, ETH
Zurich. His current research interests include
deep learning, number formats, data representations, transprecision
computing, accelerating, and scaling applications for multinode
and multi-GPU settings.
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Verlag / D-78465 Konstanz / Germany
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(0) 7533 97227 Telefax: +49 (0) 7533
97228
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