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Der Campus im Frühling. Bildinformationen anzeigen

Der Campus im Frühling.

Foto: Universität Paderborn, Kamil Glabica.

Muhammad Awais

Kontakt
Publikationen
 Muhammad Awais

Technische Informatik

Doktorand

Telefon:
+49 5251 60-4348
Büro:
O3.125
Web:
Besucher:
Pohlweg 51
33098 Paderborn

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2020

A Hybrid Synthesis Methodology for Approximate Circuits

M. Awais, H. Ghasemzadeh Mohammadi, M. Platzner, in: Proceedings of the 30th ACM Great Lakes Symposium on VLSI (GLSVLSI) 2020, ACM, 2020, pp. 1-6

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.


2019

CIRCA: Towards a Modular and Extensible Framework for Approximate Circuit Generation

L.M. Witschen, T. Wiersema, H. Ghasemzadeh Mohammadi, M. Awais, M. Platzner, Microelectronics Reliability (2019), 99, pp. 277-290

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.


2018

CIRCA: Towards a Modular and Extensible Framework for Approximate Circuit Generation

L.M. Witschen, T. Wiersema, H. Ghasemzadeh Mohammadi, M. Awais, M. Platzner, in: Third Workshop on Approximate Computing (AxC 2018), 2018

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.


An MCTS-based Framework for Synthesis of Approximate Circuits

M. Awais, H. Ghasemzadeh Mohammadi, M. Platzner, in: 26th IFIP/IEEE International Conference on Very Large Scale Integration (VLSI-SoC), 2018, pp. 219-224

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.


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