Design and Challenges of Edge Computing ASICs on Front-End Electronics Article Swipe
Sandeep Miryala
,
G. Carini
,
G. Deptuch
,
Jin Huang
,
Srinivas Katkoori
,
P. Maj
,
Soumyajit Mandal
,
Yihui Ren
,
Md Adnan Zaman
·
YOU?
·
· 2022
· Open Access
·
· DOI: https://doi.org/10.1109/isqed54688.2022.9806248
YOU?
·
· 2022
· Open Access
·
· DOI: https://doi.org/10.1109/isqed54688.2022.9806248
In situ or hardware-embedded data processing of raw signals, close to their source, in radiation detectors is expected to provide dramatic improvements in data quality and volumes. However, the implementation of artificial neural networks (ANNs) in the front-end electronics, and the design of custom integrated circuits (ASICs), comes with challenges. In addition, detectors have to operate with limited power budget and implement complex functionalities in a very dense space. They often are exposed to extreme conditions as they work in high-radiation environments and/or cryogenic temperatures. This paper presents examples of applications and design methodologies for in-situ ANNs, along with the challenges of retaining the fidelity of the trained networks. For illustration, we use the problem of estimating the energy deposited by the radiation from digitized waveforms. The proposed implementation starts with an ML algorithm trained in Qkeras and eventually leads to an equivalent ASIC implementation. The associated design challenges in realizing energy and area efficient implementations in CMOS processes are reviewed. Novel approaches that employ hybrid technologies (combination of CMOS with memristors), in-memory computing models, and bio-inspired spiking neural networks are also highlighted
Related Topics
Concepts
Application-specific integrated circuit
Computer science
Electronics
CMOS
Front and back ends
Computer architecture
Field-programmable gate array
Embedded system
Artificial neural network
Electronic engineering
Computer hardware
Electrical engineering
Engineering
Artificial intelligence
Operating system
Metadata
- Type
- article
- Language
- en
- Landing Page
- https://doi.org/10.1109/isqed54688.2022.9806248
- OA Status
- green
- Cited By
- 2
- References
- 50
- Related Works
- 10
- OpenAlex ID
- https://openalex.org/W4283717476
All OpenAlex metadata
Raw OpenAlex JSON
- OpenAlex ID
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https://openalex.org/W4283717476Canonical identifier for this work in OpenAlex
- DOI
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https://doi.org/10.1109/isqed54688.2022.9806248Digital Object Identifier
- Title
-
Design and Challenges of Edge Computing ASICs on Front-End ElectronicsWork title
- Type
-
articleOpenAlex work type
- Language
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enPrimary language
- Publication year
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2022Year of publication
- Publication date
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2022-04-06Full publication date if available
- Authors
-
Sandeep Miryala, G. Carini, G. Deptuch, Jin Huang, Srinivas Katkoori, P. Maj, Soumyajit Mandal, Yihui Ren, Md Adnan ZamanList of authors in order
- Landing page
-
https://doi.org/10.1109/isqed54688.2022.9806248Publisher landing page
- Open access
-
YesWhether a free full text is available
- OA status
-
greenOpen access status per OpenAlex
- OA URL
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https://www.osti.gov/biblio/1906165Direct OA link when available
- Concepts
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Application-specific integrated circuit, Computer science, Electronics, CMOS, Front and back ends, Computer architecture, Field-programmable gate array, Embedded system, Artificial neural network, Electronic engineering, Computer hardware, Electrical engineering, Engineering, Artificial intelligence, Operating systemTop concepts (fields/topics) attached by OpenAlex
- Cited by
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2Total citation count in OpenAlex
- Citations by year (recent)
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2025: 1, 2023: 1Per-year citation counts (last 5 years)
- References (count)
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10Other works algorithmically related by OpenAlex
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