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Hardware and Software Generation from Large Actor Machines in Streaming Applications

Callanan, Gareth LU and Gruian, Flavius LU orcid (2024) 39th ACM/SIGAPP Symposium on Applied Computing, SAC '24 p.142-142
Abstract
Streaming applications, such as MPEG video encoders or sensor processing pipelines, are increasing in complexity as well as the diversity of platforms that they run on. The toolchains handling these applications must keep up with this increase at all levels of abstraction. The Actor Machine (AM) is an intermediate representation in the toolchain that we make use of. Large AMs are difficult to work with due to an extreme number of states having to be explored for implementation. In this work we add don't care conditions to the AM model and devise a novel state-reducer algorithm by assigning priorities to conditions. This allows for the compilation of larger AMs in the frontend. These AMs execute faster in software when compared to AMs from... (More)
Streaming applications, such as MPEG video encoders or sensor processing pipelines, are increasing in complexity as well as the diversity of platforms that they run on. The toolchains handling these applications must keep up with this increase at all levels of abstraction. The Actor Machine (AM) is an intermediate representation in the toolchain that we make use of. Large AMs are difficult to work with due to an extreme number of states having to be explored for implementation. In this work we add don't care conditions to the AM model and devise a novel state-reducer algorithm by assigning priorities to conditions. This allows for the compilation of larger AMs in the frontend. These AMs execute faster in software when compared to AMs from alternative state-reducer algorithms. We discover and describe bottlenecks in the downstream tools when compiling to hardware that need to be addressed. (Less)
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author
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organization
publishing date
type
Chapter in Book/Report/Conference proceeding
publication status
published
subject
host publication
SAC'24: Proceedings of the 39th ACM/SIGAPP Symposium on Applied Computing
pages
150 pages
publisher
Association for Computing Machinery (ACM)
conference name
39th ACM/SIGAPP Symposium on Applied Computing, SAC '24
conference location
Avila, Spain
conference dates
2024-04-08 - 2024-04-12
ISBN
9798400702433
DOI
10.1145/3605098.3635930
language
English
LU publication?
yes
id
e3f7baf2-a0ac-4a0a-b66c-e0977a8d239e
date added to LUP
2024-05-22 09:50:30
date last changed
2024-05-23 12:13:37
@inproceedings{e3f7baf2-a0ac-4a0a-b66c-e0977a8d239e,
  abstract     = {{Streaming applications, such as MPEG video encoders or sensor processing pipelines, are increasing in complexity as well as the diversity of platforms that they run on. The toolchains handling these applications must keep up with this increase at all levels of abstraction. The Actor Machine (AM) is an intermediate representation in the toolchain that we make use of. Large AMs are difficult to work with due to an extreme number of states having to be explored for implementation. In this work we add don't care conditions to the AM model and devise a novel state-reducer algorithm by assigning priorities to conditions. This allows for the compilation of larger AMs in the frontend. These AMs execute faster in software when compared to AMs from alternative state-reducer algorithms. We discover and describe bottlenecks in the downstream tools when compiling to hardware that need to be addressed.}},
  author       = {{Callanan, Gareth and Gruian, Flavius}},
  booktitle    = {{SAC'24: Proceedings of the 39th ACM/SIGAPP Symposium on Applied Computing}},
  isbn         = {{9798400702433}},
  language     = {{eng}},
  month        = {{05}},
  pages        = {{142--142}},
  publisher    = {{Association for Computing Machinery (ACM)}},
  title        = {{Hardware and Software Generation from Large Actor Machines in Streaming Applications}},
  url          = {{http://dx.doi.org/10.1145/3605098.3635930}},
  doi          = {{10.1145/3605098.3635930}},
  year         = {{2024}},
}