cfaed Publications

SoC programming in the era of the Internet of Things, machine learning and emerging technologies

Reference

Jeronimo Castrillon, "SoC programming in the era of the Internet of Things, machine learning and emerging technologies" , In Groupement De Recherche SOC2: System On Chip, Systèmes embarqués et Objets Connecté (keynote), Jun 2019.

Abstract

The design of a system on chip has traditionally been one of the most complex tasks in computing systems. Designers have to deal with stringent application constraints under a low-power budget while reducing non-recurring engineering costs. Modeling languages, costs models of hardware, system simulators, design exploration methodologies and alike have made it possible to cope with this high complexity. Today, three recent trends represent a non trivial complexity increase and thus a challenge for SoC designers and programmers, namely, 1) additional system dynamics in the context of the Internet of Things, 2) the ubiquity of machine learning workloads, and 3) the added complexity brought by specialization and emerging technologies. This talk discusses how models and higher level programming abstractions can be leveraged to cope with these trends. A dataflow programming methodology is extended to account for dynamic execution scenarios at runtime. A tensor abstraction, common in machine learning, is introduced that eases programming and design tasks. Finally, the talk shows how the tensor abstraction is useful to efficiently map tensor computations to SoCs with non-volatile racetrack scratch-pad memory.

Bibtex

@Misc{castrillon_gdrsoc2019,
author = {Castrillon, Jeronimo},
title = {SoC programming in the era of the Internet of Things, machine learning and emerging technologies},
howpublished = {Groupement De Recherche SOC2: System On Chip, Syst{\`e}mes embarqu{\'e}s et Objets Connect{\'e} (keynote)},
month = jun,
year = {2019},
abstract = {The design of a system on chip has traditionally been one of the most complex tasks in computing
systems. Designers have to deal with stringent application constraints under a low-power budget while
reducing non-recurring engineering costs. Modeling languages, costs models of hardware, system
simulators, design exploration methodologies and alike have made it possible to cope with this high
complexity. Today, three recent trends represent a non trivial complexity increase and thus a challenge
for SoC designers and programmers, namely, 1) additional system dynamics in the context of the
Internet of Things, 2) the ubiquity of machine learning workloads, and 3) the added complexity brought
by specialization and emerging technologies. This talk discusses how models and higher level
programming abstractions can be leveraged to cope with these trends. A dataflow programming
methodology is extended to account for dynamic execution scenarios at runtime. A tensor abstraction,
common in machine learning, is introduced that eases programming and design tasks. Finally, the
talk shows how the tensor abstraction is useful to efficiently map tensor computations to SoCs with
non-volatile racetrack scratch-pad memory.},
location = {Montpellier, France},
url = {http://www.gdr-soc.cnrs.fr/programme-colloque-2019/}
}

Downloads

190620_castrillon_gdrsoc2-lowres [PDF]

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https://cfaed.tu-dresden.de/publications?pubId=2475


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