@inproceedings{54468,
  author       = {{Awais, Muhammad and Ghasemzadeh Mohammadi, Hassan and Platzner, Marco}},
  booktitle    = {{To apear in IEEE ISVLSI 2024}},
  location     = {{Knoxville, Tennessee, USA}},
  title        = {{{DeepApprox: Rapid Deep Learning based Design Space Exploration of Approximate Circuits via Check-pointing}}},
  year         = {{2024}},
}

@inproceedings{21610,
  author       = {{Awais, Muhammad and Ghasemzadeh Mohammadi, Hassan and Platzner, Marco}},
  booktitle    = {{Proceedings of the ACM Great Lakes Symposium on VLSI (GLSVLSI) 2021}},
  location     = {{Virtual}},
  pages        = {{27--32}},
  publisher    = {{ACM}},
  title        = {{{LDAX: A Learning-based Fast Design Space Exploration Framework for Approximate Circuit Synthesis}}},
  doi          = {{https://doi.org/10.1145/3453688.3461506}},
  year         = {{2021}},
}

@inproceedings{22309,
  abstract     = {{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       = {{Awais, Muhammad and Platzner, Marco}},
  booktitle    = {{Proceedings of IEEE Computer Society Annual Symposium on VLSI}},
  keywords     = {{Approximate computing, Design space exploration, Accelerator synthesis}},
  location     = {{Tampa, Florida USA (Virtual)}},
  pages        = {{384--389}},
  publisher    = {{IEEE}},
  title        = {{{MCTS-Based Synthesis Towards Efficient Approximate Accelerators}}},
  year         = {{2021}},
}

@inproceedings{16213,
  abstract     = {{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       = {{Awais, Muhammad and Ghasemzadeh Mohammadi, Hassan and Platzner, Marco}},
  booktitle    = {{Proceedings of the 30th ACM Great Lakes Symposium on VLSI (GLSVLSI) 2020}},
  location     = {{Beijing, China}},
  pages        = {{421--426}},
  publisher    = {{ACM}},
  title        = {{{A Hybrid Synthesis Methodology for Approximate Circuits}}},
  doi          = {{10.1145/3386263.3406952}},
  year         = {{2020}},
}

@article{3585,
  abstract     = {{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       = {{Witschen, Linus Matthias and Wiersema, Tobias and Ghasemzadeh Mohammadi, Hassan and Awais, Muhammad and Platzner, Marco}},
  issn         = {{0026-2714}},
  journal      = {{Microelectronics Reliability}},
  keywords     = {{Approximate Computing, Framework, Pareto Front, Accuracy}},
  pages        = {{277--290}},
  publisher    = {{Elsevier}},
  title        = {{{CIRCA: Towards a Modular and Extensible Framework for Approximate Circuit Generation}}},
  doi          = {{10.1016/j.microrel.2019.04.003}},
  volume       = {{99}},
  year         = {{2019}},
}

@unpublished{3586,
  abstract     = {{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       = {{Witschen, Linus Matthias and Wiersema, Tobias and Ghasemzadeh Mohammadi, Hassan and Awais, Muhammad and Platzner, Marco}},
  booktitle    = {{Third Workshop on Approximate Computing (AxC 2018)}},
  keywords     = {{Approximate Computing, Framework, Pareto Front, Accuracy}},
  pages        = {{6}},
  title        = {{{CIRCA: Towards a Modular and Extensible Framework for Approximate Circuit Generation}}},
  year         = {{2018}},
}

@inproceedings{10598,
  abstract     = {{Approximate computing has become a very popular design
strategy that exploits error resilient computations to achieve higher
performance and energy efficiency. Automated synthesis of approximate
circuits is performed via functional approximation, in which various
parts of the target circuit are extensively examined with a library
of approximate components/transformations to trade off the functional
accuracy and computational budget (i.e., power). However, as the number
of possible approximate transformations increases, traditional search
techniques suffer from a combinatorial explosion due to the large
branching factor. In this work, we present a comprehensive framework
for automated synthesis of approximate circuits from either structural
or behavioral descriptions. We adapt the Monte Carlo Tree Search
(MCTS), as a stochastic search technique, to deal with the large design
space exploration, which enables a broader range of potential possible
approximations through lightweight random simulations. The proposed
framework is able to recognize the design Pareto set even with low
computational budgets. Experimental results highlight the capabilities of
the proposed synthesis framework by resulting in up to 61.69% energy
saving while maintaining the predefined quality constraints.}},
  author       = {{Awais, Muhammad and Ghasemzadeh Mohammadi, Hassan and Platzner, Marco}},
  booktitle    = {{26th IFIP/IEEE International Conference on Very Large Scale Integration (VLSI-SoC)}},
  keywords     = {{Approximate computing, High-level synthesis, Accuracy, Monte-Carlo tree search, Circuit simulation}},
  pages        = {{219--224}},
  title        = {{{An MCTS-based Framework for Synthesis of Approximate Circuits}}},
  doi          = {{10.1109/VLSI-SoC.2018.8645026}},
  year         = {{2018}},
}