---
_id: '54468'
author:
- first_name: Muhammad
  full_name: Awais, Muhammad
  id: '64665'
  last_name: Awais
  orcid: https://orcid.org/0000-0003-4148-2969
- first_name: Hassan
  full_name: Ghasemzadeh Mohammadi, Hassan
  id: '61186'
  last_name: Ghasemzadeh Mohammadi
- first_name: Marco
  full_name: Platzner, Marco
  id: '398'
  last_name: Platzner
citation:
  ama: 'Awais M, Ghasemzadeh Mohammadi H, Platzner M. DeepApprox: Rapid Deep Learning
    based Design Space Exploration of Approximate Circuits via Check-pointing. In:
    <i>To Apear in IEEE ISVLSI 2024</i>. ; 2024.'
  apa: 'Awais, M., Ghasemzadeh Mohammadi, H., &#38; Platzner, M. (2024). DeepApprox:
    Rapid Deep Learning based Design Space Exploration of Approximate Circuits via
    Check-pointing. <i>To Apear in IEEE ISVLSI 2024</i>. IEEE Computer Society Annual
    Symposium on VLSI, Knoxville, Tennessee, USA.'
  bibtex: '@inproceedings{Awais_Ghasemzadeh Mohammadi_Platzner_2024, title={DeepApprox:
    Rapid Deep Learning based Design Space Exploration of Approximate Circuits via
    Check-pointing}, booktitle={To apear in IEEE ISVLSI 2024}, author={Awais, Muhammad
    and Ghasemzadeh Mohammadi, Hassan and Platzner, Marco}, year={2024} }'
  chicago: 'Awais, Muhammad, Hassan Ghasemzadeh Mohammadi, and Marco Platzner. “DeepApprox:
    Rapid Deep Learning Based Design Space Exploration of Approximate Circuits via
    Check-Pointing.” In <i>To Apear in IEEE ISVLSI 2024</i>, 2024.'
  ieee: 'M. Awais, H. Ghasemzadeh Mohammadi, and M. Platzner, “DeepApprox: Rapid Deep
    Learning based Design Space Exploration of Approximate Circuits via Check-pointing,”
    presented at the IEEE Computer Society Annual Symposium on VLSI, Knoxville, Tennessee,
    USA, 2024.'
  mla: 'Awais, Muhammad, et al. “DeepApprox: Rapid Deep Learning Based Design Space
    Exploration of Approximate Circuits via Check-Pointing.” <i>To Apear in IEEE ISVLSI
    2024</i>, 2024.'
  short: 'M. Awais, H. Ghasemzadeh Mohammadi, M. Platzner, in: To Apear in IEEE ISVLSI
    2024, 2024.'
conference:
  end_date: 2024-07-03
  location: Knoxville, Tennessee, USA
  name: IEEE Computer Society Annual Symposium on VLSI
  start_date: 2024-07-01
date_created: 2024-05-28T08:14:43Z
date_updated: 2024-05-29T08:26:29Z
department:
- _id: '78'
language:
- iso: eng
publication: To apear in IEEE ISVLSI 2024
status: public
title: 'DeepApprox: Rapid Deep Learning based Design Space Exploration of Approximate
  Circuits via Check-pointing'
type: conference
user_id: '64665'
year: '2024'
...
---
_id: '21610'
author:
- first_name: Muhammad
  full_name: Awais, Muhammad
  id: '64665'
  last_name: Awais
  orcid: https://orcid.org/0000-0003-4148-2969
- first_name: Hassan
  full_name: Ghasemzadeh Mohammadi, Hassan
  id: '61186'
  last_name: Ghasemzadeh Mohammadi
- first_name: Marco
  full_name: Platzner, Marco
  id: '398'
  last_name: Platzner
citation:
  ama: 'Awais M, Ghasemzadeh Mohammadi H, Platzner M. LDAX: A Learning-based Fast
    Design Space Exploration Framework for Approximate Circuit Synthesis. In: <i>Proceedings
    of the ACM Great Lakes Symposium on VLSI (GLSVLSI) 2021</i>. ACM; 2021:27-32.
    doi:<a href="https://doi.org/10.1145/3453688.3461506">https://doi.org/10.1145/3453688.3461506</a>'
  apa: 'Awais, M., Ghasemzadeh Mohammadi, H., &#38; Platzner, M. (2021). LDAX: A Learning-based
    Fast Design Space Exploration Framework for Approximate Circuit Synthesis. In
    <i>Proceedings of the ACM Great Lakes Symposium on VLSI (GLSVLSI) 2021</i> (pp.
    27–32). Virtual: ACM. <a href="https://doi.org/10.1145/3453688.3461506">https://doi.org/10.1145/3453688.3461506</a>'
  bibtex: '@inproceedings{Awais_Ghasemzadeh Mohammadi_Platzner_2021, title={LDAX:
    A Learning-based Fast Design Space Exploration Framework for Approximate Circuit
    Synthesis}, DOI={<a href="https://doi.org/10.1145/3453688.3461506">https://doi.org/10.1145/3453688.3461506</a>},
    booktitle={Proceedings of the ACM Great Lakes Symposium on VLSI (GLSVLSI) 2021},
    publisher={ACM}, author={Awais, Muhammad and Ghasemzadeh Mohammadi, Hassan and
    Platzner, Marco}, year={2021}, pages={27–32} }'
  chicago: 'Awais, Muhammad, Hassan Ghasemzadeh Mohammadi, and Marco Platzner. “LDAX:
    A Learning-Based Fast Design Space Exploration Framework for Approximate Circuit
    Synthesis.” In <i>Proceedings of the ACM Great Lakes Symposium on VLSI (GLSVLSI)
    2021</i>, 27–32. ACM, 2021. <a href="https://doi.org/10.1145/3453688.3461506">https://doi.org/10.1145/3453688.3461506</a>.'
  ieee: 'M. Awais, H. Ghasemzadeh Mohammadi, and M. Platzner, “LDAX: A Learning-based
    Fast Design Space Exploration Framework for Approximate Circuit Synthesis,” in
    <i>Proceedings of the ACM Great Lakes Symposium on VLSI (GLSVLSI) 2021</i>, Virtual,
    2021, pp. 27–32.'
  mla: 'Awais, Muhammad, et al. “LDAX: A Learning-Based Fast Design Space Exploration
    Framework for Approximate Circuit Synthesis.” <i>Proceedings of the ACM Great
    Lakes Symposium on VLSI (GLSVLSI) 2021</i>, ACM, 2021, pp. 27–32, doi:<a href="https://doi.org/10.1145/3453688.3461506">https://doi.org/10.1145/3453688.3461506</a>.'
  short: 'M. Awais, H. Ghasemzadeh Mohammadi, M. Platzner, in: Proceedings of the
    ACM Great Lakes Symposium on VLSI (GLSVLSI) 2021, ACM, 2021, pp. 27–32.'
conference:
  end_date: 2021-06-25
  location: Virtual
  name: 31st ACM Great Lakes Symposium on VLSI (GLSVLSI) 2021
  start_date: 2021-06-22
date_created: 2021-04-13T10:17:47Z
date_updated: 2022-01-06T06:55:07Z
department:
- _id: '78'
doi: https://doi.org/10.1145/3453688.3461506
language:
- iso: eng
page: 27-32
publication: Proceedings of the ACM Great Lakes Symposium on VLSI (GLSVLSI) 2021
publication_status: published
publisher: ACM
status: public
title: 'LDAX: A Learning-based Fast Design Space Exploration Framework for Approximate
  Circuit Synthesis'
type: conference
user_id: '64665'
year: '2021'
...
---
_id: '22309'
abstract:
- lang: eng
  text: Approximate computing (AC) has acquired significant maturity in recent years
    as a promising approach to obtain energy and area-efficient hardware. Automated
    approximate accelerator synthesis involves a great deal of complexity on the size
    of design space which exponentially grows with the number of possible approximations.
    Design space exploration of approximate accelerator synthesis is usually targeted
    via heuristic-based search methods. The majority of existing frameworks prune
    a large part of the design space using a greedy-based approach to keep the problem
    tractable. Therefore, they result in inferior solutions since many potential solutions
    are neglected in the pruning process without the possibility of backtracking of
    removed approximate instances. In this paper, we address the aforementioned issue
    by adopting Monte Carlo Tree Search (MCTS), as an efficient stochastic learning-based
    search algorithm, in the context of automated synthesis of approximate accelerators.
    This enables the synthesis frameworks to deeply subsamples the design space of
    approximate accelerator synthesis toward most promising approximate instances
    based on the required performance goals, i.e., power consumption, area, or/and
    delay. We investigated the challenges of providing an efficient open-source framework
    that benefits analytical and search-based approximation techniques simultaneously
    to both speed up the synthesis runtime and improve the quality of obtained results.
    Besides, we studied the utilization of machine learning algorithms to improve
    the performance of several critical steps, i.e., accelerator quality testing,
    in the synthesis framework. The proposed framework can help the community to rapidly
    generate efficient approximate accelerators in a reasonable runtime.
author:
- first_name: Muhammad
  full_name: Awais, Muhammad
  id: '64665'
  last_name: Awais
  orcid: https://orcid.org/0000-0003-4148-2969
- first_name: Marco
  full_name: Platzner, Marco
  last_name: Platzner
citation:
  ama: 'Awais M, Platzner M. MCTS-Based Synthesis Towards Efficient Approximate Accelerators.
    In: <i>Proceedings of IEEE Computer Society Annual Symposium on VLSI</i>. IEEE;
    2021:384-389.'
  apa: Awais, M., &#38; Platzner, M. (2021). MCTS-Based Synthesis Towards Efficient
    Approximate Accelerators. <i>Proceedings of IEEE Computer Society Annual Symposium
    on VLSI</i>, 384–389.
  bibtex: '@inproceedings{Awais_Platzner_2021, title={MCTS-Based Synthesis Towards
    Efficient Approximate Accelerators}, booktitle={Proceedings of IEEE Computer Society
    Annual Symposium on VLSI}, publisher={IEEE}, author={Awais, Muhammad and Platzner,
    Marco}, year={2021}, pages={384–389} }'
  chicago: Awais, Muhammad, and Marco Platzner. “MCTS-Based Synthesis Towards Efficient
    Approximate Accelerators.” In <i>Proceedings of IEEE Computer Society Annual Symposium
    on VLSI</i>, 384–89. IEEE, 2021.
  ieee: M. Awais and M. Platzner, “MCTS-Based Synthesis Towards Efficient Approximate
    Accelerators,” in <i>Proceedings of IEEE Computer Society Annual Symposium on
    VLSI</i>, Tampa, Florida USA (Virtual), 2021, pp. 384–389.
  mla: Awais, Muhammad, and Marco Platzner. “MCTS-Based Synthesis Towards Efficient
    Approximate Accelerators.” <i>Proceedings of IEEE Computer Society Annual Symposium
    on VLSI</i>, IEEE, 2021, pp. 384–89.
  short: 'M. Awais, M. Platzner, in: Proceedings of IEEE Computer Society Annual Symposium
    on VLSI, IEEE, 2021, pp. 384–389.'
conference:
  end_date: 2021-07-09
  location: Tampa, Florida USA (Virtual)
  name: IEEE Computer Society Annual Symposium on VLSI
  start_date: 2021-07-07
date_created: 2021-06-14T14:05:17Z
date_updated: 2022-01-06T06:55:31Z
department:
- _id: '78'
keyword:
- Approximate computing
- Design space exploration
- Accelerator synthesis
language:
- iso: eng
page: 384-389
publication: Proceedings of IEEE Computer Society Annual Symposium on VLSI
publisher: IEEE
status: public
title: MCTS-Based Synthesis Towards Efficient Approximate Accelerators
type: conference
user_id: '64665'
year: '2021'
...
---
_id: '16213'
abstract:
- lang: eng
  text: 'Automated synthesis of approximate circuits via functional approximations
    is of prominent importance to provide efficiency in energy, runtime, and chip
    area required to execute an application. Approximate circuits are usually obtained
    either through analytical approximation methods leveraging approximate transformations
    such as bit-width scaling or via iterative search-based optimization methods when
    a library of approximate components, e.g., approximate adders and multipliers,
    is available. For the latter, exploring the extremely large design space is challenging
    in terms of both computations and quality of results. While the combination of
    both methods can create more room for further approximations, the \textit{Design
    Space Exploration}~(DSE) becomes a crucial issue. In this paper, we present such
    a hybrid synthesis methodology that applies a low-cost analytical method followed
    by parallel stochastic search-based optimization. We address the DSE challenge
    through efficient pruning of the design space and skipping unnecessary expensive
    testing and/or verification steps. The experimental results reveal up to 10.57x
    area savings in comparison with both purely analytical or search-based approaches. '
author:
- first_name: Muhammad
  full_name: Awais, Muhammad
  id: '64665'
  last_name: Awais
  orcid: https://orcid.org/0000-0003-4148-2969
- first_name: Hassan
  full_name: Ghasemzadeh Mohammadi, Hassan
  id: '61186'
  last_name: Ghasemzadeh Mohammadi
- first_name: Marco
  full_name: Platzner, Marco
  id: '398'
  last_name: Platzner
citation:
  ama: 'Awais M, Ghasemzadeh Mohammadi H, Platzner M. A Hybrid Synthesis Methodology
    for Approximate Circuits. In: <i>Proceedings of the 30th ACM Great Lakes Symposium
    on VLSI (GLSVLSI) 2020</i>. ACM; 2020:421-426. doi:<a href="https://doi.org/10.1145/3386263.3406952">10.1145/3386263.3406952</a>'
  apa: 'Awais, M., Ghasemzadeh Mohammadi, H., &#38; Platzner, M. (2020). A Hybrid
    Synthesis Methodology for Approximate Circuits. In <i>Proceedings of the 30th
    ACM Great Lakes Symposium on VLSI (GLSVLSI) 2020</i> (pp. 421–426). Beijing, China:
    ACM. <a href="https://doi.org/10.1145/3386263.3406952">https://doi.org/10.1145/3386263.3406952</a>'
  bibtex: '@inproceedings{Awais_Ghasemzadeh Mohammadi_Platzner_2020, title={A Hybrid
    Synthesis Methodology for Approximate Circuits}, DOI={<a href="https://doi.org/10.1145/3386263.3406952">10.1145/3386263.3406952</a>},
    booktitle={Proceedings of the 30th ACM Great Lakes Symposium on VLSI (GLSVLSI)
    2020}, publisher={ACM}, author={Awais, Muhammad and Ghasemzadeh Mohammadi, Hassan
    and Platzner, Marco}, year={2020}, pages={421–426} }'
  chicago: Awais, Muhammad, Hassan Ghasemzadeh Mohammadi, and Marco Platzner. “A Hybrid
    Synthesis Methodology for Approximate Circuits.” In <i>Proceedings of the 30th
    ACM Great Lakes Symposium on VLSI (GLSVLSI) 2020</i>, 421–26. ACM, 2020. <a href="https://doi.org/10.1145/3386263.3406952">https://doi.org/10.1145/3386263.3406952</a>.
  ieee: M. Awais, H. Ghasemzadeh Mohammadi, and M. Platzner, “A Hybrid Synthesis Methodology
    for Approximate Circuits,” in <i>Proceedings of the 30th ACM Great Lakes Symposium
    on VLSI (GLSVLSI) 2020</i>, Beijing, China, 2020, pp. 421–426.
  mla: Awais, Muhammad, et al. “A Hybrid Synthesis Methodology for Approximate Circuits.”
    <i>Proceedings of the 30th ACM Great Lakes Symposium on VLSI (GLSVLSI) 2020</i>,
    ACM, 2020, pp. 421–26, doi:<a href="https://doi.org/10.1145/3386263.3406952">10.1145/3386263.3406952</a>.
  short: 'M. Awais, H. Ghasemzadeh Mohammadi, M. Platzner, in: Proceedings of the
    30th ACM Great Lakes Symposium on VLSI (GLSVLSI) 2020, ACM, 2020, pp. 421–426.'
conference:
  location: Beijing, China
  name: ACM Great Lakes Symposium on VLSI (GLSVLSI) 2020
date_created: 2020-03-02T15:49:38Z
date_updated: 2022-01-06T06:52:45Z
department:
- _id: '78'
doi: 10.1145/3386263.3406952
language:
- iso: eng
page: 421-426
publication: Proceedings of the 30th ACM Great Lakes Symposium on VLSI (GLSVLSI) 2020
publication_status: published
publisher: ACM
status: public
title: A Hybrid Synthesis Methodology for Approximate Circuits
type: conference
user_id: '64665'
year: '2020'
...
---
_id: '3585'
abstract:
- lang: eng
  text: Existing approaches and tools for the generation of approximate circuits often
    lack generality and are restricted to certain circuit types, approximation techniques,
    and quality assurance methods. Moreover, only few tools are publicly available.
    This hinders the development and evaluation of new techniques for approximating
    circuits and their comparison to previous approaches. In this paper, we first analyze
    and classify related approaches and then present CIRCA, our flexible framework
    for search-based approximate circuit generation. CIRCA is developed with a focus
    on modularity and extensibility. We present the architecture of CIRCA with its
    clear separation into stages and functional blocks, report on the current prototype,
    and show initial experiments.
author:
- first_name: Linus Matthias
  full_name: Witschen, Linus Matthias
  id: '49051'
  last_name: Witschen
- first_name: Tobias
  full_name: Wiersema, Tobias
  id: '3118'
  last_name: Wiersema
- first_name: Hassan
  full_name: Ghasemzadeh Mohammadi, Hassan
  id: '61186'
  last_name: Ghasemzadeh Mohammadi
- first_name: Muhammad
  full_name: Awais, Muhammad
  id: '64665'
  last_name: Awais
  orcid: https://orcid.org/0000-0003-4148-2969
- first_name: Marco
  full_name: Platzner, Marco
  id: '398'
  last_name: Platzner
citation:
  ama: 'Witschen LM, Wiersema T, Ghasemzadeh Mohammadi H, Awais M, Platzner M. CIRCA:
    Towards a Modular and Extensible Framework for Approximate Circuit Generation.
    <i>Microelectronics Reliability</i>. 2019;99:277-290. doi:<a href="https://doi.org/10.1016/j.microrel.2019.04.003">10.1016/j.microrel.2019.04.003</a>'
  apa: 'Witschen, L. M., Wiersema, T., Ghasemzadeh Mohammadi, H., Awais, M., &#38;
    Platzner, M. (2019). CIRCA: Towards a Modular and Extensible Framework for Approximate
    Circuit Generation. <i>Microelectronics Reliability</i>, <i>99</i>, 277–290. <a
    href="https://doi.org/10.1016/j.microrel.2019.04.003">https://doi.org/10.1016/j.microrel.2019.04.003</a>'
  bibtex: '@article{Witschen_Wiersema_Ghasemzadeh Mohammadi_Awais_Platzner_2019, title={CIRCA:
    Towards a Modular and Extensible Framework for Approximate Circuit Generation},
    volume={99}, DOI={<a href="https://doi.org/10.1016/j.microrel.2019.04.003">10.1016/j.microrel.2019.04.003</a>},
    journal={Microelectronics Reliability}, publisher={Elsevier}, author={Witschen,
    Linus Matthias and Wiersema, Tobias and Ghasemzadeh Mohammadi, Hassan and Awais,
    Muhammad and Platzner, Marco}, year={2019}, pages={277–290} }'
  chicago: 'Witschen, Linus Matthias, Tobias Wiersema, Hassan Ghasemzadeh Mohammadi,
    Muhammad Awais, and Marco Platzner. “CIRCA: Towards a Modular and Extensible Framework
    for Approximate Circuit Generation.” <i>Microelectronics Reliability</i> 99 (2019):
    277–90. <a href="https://doi.org/10.1016/j.microrel.2019.04.003">https://doi.org/10.1016/j.microrel.2019.04.003</a>.'
  ieee: 'L. M. Witschen, T. Wiersema, H. Ghasemzadeh Mohammadi, M. Awais, and M. Platzner,
    “CIRCA: Towards a Modular and Extensible Framework for Approximate Circuit Generation,”
    <i>Microelectronics Reliability</i>, vol. 99, pp. 277–290, 2019.'
  mla: 'Witschen, Linus Matthias, et al. “CIRCA: Towards a Modular and Extensible
    Framework for Approximate Circuit Generation.” <i>Microelectronics Reliability</i>,
    vol. 99, Elsevier, 2019, pp. 277–90, doi:<a href="https://doi.org/10.1016/j.microrel.2019.04.003">10.1016/j.microrel.2019.04.003</a>.'
  short: L.M. Witschen, T. Wiersema, H. Ghasemzadeh Mohammadi, M. Awais, M. Platzner,
    Microelectronics Reliability 99 (2019) 277–290.
date_created: 2018-07-20T14:08:49Z
date_updated: 2022-01-06T06:59:25Z
department:
- _id: '78'
doi: 10.1016/j.microrel.2019.04.003
intvolume: '        99'
keyword:
- Approximate Computing
- Framework
- Pareto Front
- Accuracy
language:
- iso: eng
page: 277-290
project:
- _id: '12'
  name: SFB 901 - Subproject B4
- _id: '1'
  name: SFB 901
- _id: '3'
  name: SFB 901 - Project Area B
- _id: '52'
  name: Computing Resources Provided by the Paderborn Center for Parallel Computing
publication: Microelectronics Reliability
publication_identifier:
  issn:
  - 0026-2714
publication_status: published
publisher: Elsevier
status: public
title: 'CIRCA: Towards a Modular and Extensible Framework for Approximate Circuit
  Generation'
type: journal_article
user_id: '49051'
volume: 99
year: '2019'
...
---
_id: '3586'
abstract:
- lang: eng
  text: Existing approaches and tools for the generation of approximate circuits often
    lack generality and are restricted to certain circuit types, approximation techniques,
    and quality assurance methods. Moreover, only few tools are publicly available.
    This hinders the development and evaluation of new techniques for approximating
    circuits and their comparison to previous approaches. In this paper, we first analyze
    and classify related approaches and then present CIRCA, our flexible framework
    for search-based approximate circuit generation. CIRCA is developed with a focus
    on modularity and extensibility. We present the architecture of CIRCA with its
    clear separation into stages and functional blocks, report on the current prototype,
    and show initial experiments.
author:
- first_name: Linus Matthias
  full_name: Witschen, Linus Matthias
  id: '49051'
  last_name: Witschen
- first_name: Tobias
  full_name: Wiersema, Tobias
  id: '3118'
  last_name: Wiersema
- first_name: Hassan
  full_name: Ghasemzadeh Mohammadi, Hassan
  id: '61186'
  last_name: Ghasemzadeh Mohammadi
- first_name: Muhammad
  full_name: Awais, Muhammad
  id: '64665'
  last_name: Awais
  orcid: https://orcid.org/0000-0003-4148-2969
- first_name: Marco
  full_name: Platzner, Marco
  id: '398'
  last_name: Platzner
citation:
  ama: 'Witschen LM, Wiersema T, Ghasemzadeh Mohammadi H, Awais M, Platzner M. CIRCA:
    Towards a Modular and Extensible Framework for Approximate Circuit Generation.
    <i>Third Workshop on Approximate Computing (AxC 2018)</i>.'
  apa: 'Witschen, L. M., Wiersema, T., Ghasemzadeh Mohammadi, H., Awais, M., &#38;
    Platzner, M. (n.d.). CIRCA: Towards a Modular and Extensible Framework for Approximate
    Circuit Generation. <i>Third Workshop on Approximate Computing (AxC 2018)</i>.'
  bibtex: '@article{Witschen_Wiersema_Ghasemzadeh Mohammadi_Awais_Platzner, title={CIRCA:
    Towards a Modular and Extensible Framework for Approximate Circuit Generation},
    journal={Third Workshop on Approximate Computing (AxC 2018)}, author={Witschen,
    Linus Matthias and Wiersema, Tobias and Ghasemzadeh Mohammadi, Hassan and Awais,
    Muhammad and Platzner, Marco} }'
  chicago: 'Witschen, Linus Matthias, Tobias Wiersema, Hassan Ghasemzadeh Mohammadi,
    Muhammad Awais, and Marco Platzner. “CIRCA: Towards a Modular and Extensible Framework
    for Approximate Circuit Generation.” <i>Third Workshop on Approximate Computing
    (AxC 2018)</i>, n.d.'
  ieee: 'L. M. Witschen, T. Wiersema, H. Ghasemzadeh Mohammadi, M. Awais, and M. Platzner,
    “CIRCA: Towards a Modular and Extensible Framework for Approximate Circuit Generation,”
    <i>Third Workshop on Approximate Computing (AxC 2018)</i>. .'
  mla: 'Witschen, Linus Matthias, et al. “CIRCA: Towards a Modular and Extensible
    Framework for Approximate Circuit Generation.” <i>Third Workshop on Approximate
    Computing (AxC 2018)</i>.'
  short: L.M. Witschen, T. Wiersema, H. Ghasemzadeh Mohammadi, M. Awais, M. Platzner,
    Third Workshop on Approximate Computing (AxC 2018) (n.d.).
date_created: 2018-07-20T14:10:46Z
date_updated: 2022-01-06T06:59:26Z
ddc:
- '000'
department:
- _id: '78'
file:
- access_level: closed
  content_type: application/pdf
  creator: tobias82
  date_created: 2018-07-20T14:13:31Z
  date_updated: 2018-07-20T14:13:31Z
  file_id: '3587'
  file_name: WitschenWMAP2018.pdf
  file_size: 285348
  relation: main_file
  success: 1
file_date_updated: 2018-07-20T14:13:31Z
has_accepted_license: '1'
keyword:
- Approximate Computing
- Framework
- Pareto Front
- Accuracy
language:
- iso: eng
page: '6'
project:
- _id: '12'
  name: SFB 901 - Subproject B4
- _id: '1'
  name: SFB 901
- _id: '3'
  name: SFB 901 - Project Area B
- _id: '52'
  name: Computing Resources Provided by the Paderborn Center for Parallel Computing
publication: Third Workshop on Approximate Computing (AxC 2018)
publication_status: accepted
status: public
title: 'CIRCA: Towards a Modular and Extensible Framework for Approximate Circuit
  Generation'
type: preprint
user_id: '49051'
year: '2018'
...
---
_id: '10598'
abstract:
- lang: eng
  text: "Approximate computing has become a very popular design\r\nstrategy that exploits
    error resilient computations to achieve higher\r\nperformance and energy efficiency.
    Automated synthesis of approximate\r\ncircuits is performed via functional approximation,
    in which various\r\nparts of the target circuit are extensively examined with
    a library\r\nof approximate components/transformations to trade off the functional\r\naccuracy
    and computational budget (i.e., power). However, as the number\r\nof possible
    approximate transformations increases, traditional search\r\ntechniques suffer
    from a combinatorial explosion due to the large\r\nbranching factor. In this work,
    we present a comprehensive framework\r\nfor automated synthesis of approximate
    circuits from either structural\r\nor behavioral descriptions. We adapt the Monte
    Carlo Tree Search\r\n(MCTS), as a stochastic search technique, to deal with the
    large design\r\nspace exploration, which enables a broader range of potential
    possible\r\napproximations through lightweight random simulations. The proposed\r\nframework
    is able to recognize the design Pareto set even with low\r\ncomputational budgets.
    Experimental results highlight the capabilities of\r\nthe proposed synthesis framework
    by resulting in up to 61.69% energy\r\nsaving while maintaining the predefined
    quality constraints."
author:
- first_name: Muhammad
  full_name: Awais, Muhammad
  id: '64665'
  last_name: Awais
  orcid: https://orcid.org/0000-0003-4148-2969
- first_name: Hassan
  full_name: Ghasemzadeh Mohammadi, Hassan
  id: '61186'
  last_name: Ghasemzadeh Mohammadi
- first_name: Marco
  full_name: Platzner, Marco
  id: '398'
  last_name: Platzner
citation:
  ama: 'Awais M, Ghasemzadeh Mohammadi H, Platzner M. An MCTS-based Framework for
    Synthesis of Approximate Circuits. In: <i>26th IFIP/IEEE International Conference
    on Very Large Scale Integration (VLSI-SoC)</i>. ; 2018:219-224. doi:<a href="https://doi.org/10.1109/VLSI-SoC.2018.8645026">10.1109/VLSI-SoC.2018.8645026</a>'
  apa: Awais, M., Ghasemzadeh Mohammadi, H., &#38; Platzner, M. (2018). An MCTS-based
    Framework for Synthesis of Approximate Circuits. In <i>26th IFIP/IEEE International
    Conference on Very Large Scale Integration (VLSI-SoC)</i> (pp. 219–224). <a href="https://doi.org/10.1109/VLSI-SoC.2018.8645026">https://doi.org/10.1109/VLSI-SoC.2018.8645026</a>
  bibtex: '@inproceedings{Awais_Ghasemzadeh Mohammadi_Platzner_2018, title={An MCTS-based
    Framework for Synthesis of Approximate Circuits}, DOI={<a href="https://doi.org/10.1109/VLSI-SoC.2018.8645026">10.1109/VLSI-SoC.2018.8645026</a>},
    booktitle={26th IFIP/IEEE International Conference on Very Large Scale Integration
    (VLSI-SoC)}, author={Awais, Muhammad and Ghasemzadeh Mohammadi, Hassan and Platzner,
    Marco}, year={2018}, pages={219–224} }'
  chicago: Awais, Muhammad, Hassan Ghasemzadeh Mohammadi, and Marco Platzner. “An
    MCTS-Based Framework for Synthesis of Approximate Circuits.” In <i>26th IFIP/IEEE
    International Conference on Very Large Scale Integration (VLSI-SoC)</i>, 219–24,
    2018. <a href="https://doi.org/10.1109/VLSI-SoC.2018.8645026">https://doi.org/10.1109/VLSI-SoC.2018.8645026</a>.
  ieee: M. Awais, H. Ghasemzadeh Mohammadi, and M. Platzner, “An MCTS-based Framework
    for Synthesis of Approximate Circuits,” in <i>26th IFIP/IEEE International Conference
    on Very Large Scale Integration (VLSI-SoC)</i>, 2018, pp. 219–224.
  mla: Awais, Muhammad, et al. “An MCTS-Based Framework for Synthesis of Approximate
    Circuits.” <i>26th IFIP/IEEE International Conference on Very Large Scale Integration
    (VLSI-SoC)</i>, 2018, pp. 219–24, doi:<a href="https://doi.org/10.1109/VLSI-SoC.2018.8645026">10.1109/VLSI-SoC.2018.8645026</a>.
  short: 'M. Awais, H. Ghasemzadeh Mohammadi, M. Platzner, in: 26th IFIP/IEEE International
    Conference on Very Large Scale Integration (VLSI-SoC), 2018, pp. 219–224.'
date_created: 2019-07-10T09:21:38Z
date_updated: 2022-01-06T06:50:46Z
department:
- _id: '78'
doi: 10.1109/VLSI-SoC.2018.8645026
keyword:
- Approximate computing
- High-level synthesis
- Accuracy
- Monte-Carlo tree search
- Circuit simulation
language:
- iso: eng
page: 219-224
publication: 26th IFIP/IEEE International Conference on Very Large Scale Integration
  (VLSI-SoC)
status: public
title: An MCTS-based Framework for Synthesis of Approximate Circuits
type: conference
user_id: '64665'
year: '2018'
...