DeepApprox: Rapid Deep Learning based Design Space Exploration of Approximate Circuits via Check-pointing - Awais, Muhammad; Ghasemzadeh Mohammadi, Hassan; Platzner, Marco (Proceedings of the IEEE Symposium on VLSI)

Flexible Industrial Analytics on Reconfigurable Systems-On-Chip - Boschmann, Alexander; Clausing, Lennart; Jentzsch, Felix; Ghasemzadeh Mohammadi, Hassan; Platzner, Marco (On-The-Fly Computing -- Individualized IT-services in dynamic markets)

LDAX: A Learning-based Fast Design Space Exploration Framework for Approximate Circuit Synthesis - Awais, Muhammad; Ghasemzadeh Mohammadi, Hassan; Platzner, Marco (Proceedings of the ACM Great Lakes Symposium on VLSI (GLSVLSI) 2021)

FLight: FPGA Acceleration of Lightweight DNN Model Inference in Industrial Analytics - Ghasemzadeh Mohammadi, Hassan; Jentzsch, Felix; Kuschel, Maurice; Arshad, Rahil; Rautmare, Sneha; Manjunatha, Suraj; Platzner, Marco; Boschmann, Alexander; Schollbach, Dirk (Machine Learning and Principles and Practice of Knowledge Discovery in Databases)

A Hybrid Synthesis Methodology for Approximate Circuits - Awais, Muhammad; Ghasemzadeh Mohammadi, Hassan; Platzner, Marco (Proceedings of the 30th ACM Great Lakes Symposium on VLSI (GLSVLSI) 2020)
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 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.

DeepWind: An Accurate Wind Turbine Condition Monitoring Framework via Deep Learning on Embedded Platforms - Ghasemzadeh Mohammadi, Hassan; Arshad, Rahil; Rautmare, Sneha; Manjunatha, Suraj; Kuschel, Maurice; Jentzsch, Felix Paul; Platzner, Marco; Boschmann, Alexander; Schollbach, Dirk (2020 25th IEEE International Conference on Emerging Technologies and Factory Automation (ETFA))

CIRCA: Towards a Modular and Extensible Framework for Approximate Circuit Generation - Witschen, Linus Matthias; Wiersema, Tobias; Ghasemzadeh Mohammadi, Hassan; Awais, Muhammad; Platzner, Marco (Microelectronics Reliability)
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.
Keywords: Approximate Computing; Framework; Pareto Front; Accuracy

Jump Search: A Fast Technique for the Synthesis of Approximate Circuits - Witschen, Linus Matthias; Ghasemzadeh Mohammadi, Hassan; Artmann, Matthias; Platzner, Marco (Fourth Workshop on Approximate Computing (AxC 2019))
State-of-the-art frameworks for generating approximate circuits usually rely on information gained through circuit synthesis and/or verification to explore the search space and to find an optimal solution. Throughout the process, a large number of circuits may be subject to processing, leading to considerable runtimes. In this work, we propose a search which takes error bounds and pre-computed impact factors into account to reduce the number of invoked synthesis and verification processes. In our experimental results, we achieved speed-ups of up to 76x while area savings remain comparable to the reference search method, simulated annealing.
Keywords: Approximate computing; parameter selection; search space exploration; verification; circuit synthesis

Jump Search: A Fast Technique for the Synthesis of Approximate Circuits - Witschen, Linus Matthias; Ghasemzadeh Mohammadi, Hassan; Artmann, Matthias; Platzner, Marco (Proceedings of the 2019 on Great Lakes Symposium on VLSI - GLSVLSI '19)
State-of-the-art frameworks for generating approximate circuits automatically explore the search space in an iterative process - often greedily. Synthesis and verification processes are invoked in each iteration to evaluate the found solutions and to guide the search algorithm. As a result, a large number of approximate circuits is subjected to analysis - leading to long runtimes - but only a few approximate circuits might form an acceptable solution. In this paper, we present our Jump Search (JS) method which seeks to reduce the runtime of an approximation process by reducing the number of expensive synthesis and verification steps. To reduce the runtime, JS computes impact factors for each approximation candidate in the circuit to create a selection of approximate circuits without invoking synthesis or verification processes. We denote the selection as path from which JS determines the final solution. In our experimental results, JS achieved speed-ups of up to 57x while area savings remain comparable to the reference search method, Simulated Annealing.
Keywords: Approximate computing; design automation; parameter selection; circuit synthesis

CIRCA: Towards a Modular and Extensible Framework for Approximate Circuit Generation - Witschen, Linus Matthias; Wiersema, Tobias; Ghasemzadeh Mohammadi, Hassan; Awais, Muhammad; Platzner, Marco (Third Workshop on Approximate Computing (AxC 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.
Keywords: Approximate Computing; Framework; Pareto Front; Accuracy