Empirical Evaluation of Model-Based Performance Prediction Methods in Software Development

Field-programmable gate arrays (FPGAs) provide a promising technology that can improve performance of many high-performance computing and embedded applications. However, unlike software design tools, the relatively immature state of FPGA tools significantly limits productivity and consequently prevents widespread adoption of the technology. For example, the lengthy design-translate-execute (DTE) process often must be iterated to meet the application requirements. Previous works have enabled model-based, design-space exploration to reduce DTE iterations but are limited by a lack of accurate model-based prediction of key design parameters, the most important of which is clock frequency. In this paper, we present a core-level modeling and design (CMD) methodology that enables modeling of FPGA applications at an abstract level and yet produces accurate predictions of parameters such as clock frequency, resource utilization (i.e., area), and latency. We evaluate CMD’s prediction methods using several high-performance DSP applications on various families of FPGAs and show an average clock-frequency prediction error of 3.6%, with a worst-case error of 20.4%, compared to the best of existing high-level prediction methods, 13.9% average error with 48.2% worst-case error. We also demonstrate how such prediction enables accurate design-space exploration without coding in a hardware-description language (HDL), significantly reducing the total design time.

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Future Automotive Embedded Systems Enabled by Efficient Model-Based Software Development

10.4271/2021-01-0129 ◽

2021 ◽

Author(s):

Juergen Schaefer ◽

Herbert Christlbauer ◽

Alexander Schreiber ◽

Graham Reith ◽

Mischa Jonker ◽

...

Keyword(s):

Embedded Systems ◽

Software Development ◽

Model Based

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Stream-based explainable recommendations via blockchain profiling

Integrated Computer-Aided Engineering ◽

10.3233/ica-210668 ◽

2021 ◽

pp. 1-17

Author(s):

Fátima Leal ◽

Bruno Veloso ◽

Benedita Malheiro ◽

Juan Carlos Burguillo ◽

Adriana E. Chis ◽

...

Keyword(s):

Historical Data ◽

Positive Impact ◽

Empirical Evaluation ◽

Recommendation Systems ◽

Use Cases ◽

Collaborative Filter ◽

Model Based ◽

Collaborative Recommendation ◽

Smart Contract ◽

User Trust

Explainable recommendations enable users to understand why certain items are suggested and, ultimately, nurture system transparency, trustworthiness, and confidence. Large crowdsourcing recommendation systems ought to crucially promote authenticity and transparency of recommendations. To address such challenge, this paper proposes the use of stream-based explainable recommendations via blockchain profiling. Our contribution relies on chained historical data to improve the quality and transparency of online collaborative recommendation filters – Memory-based and Model-based – using, as use cases, data streamed from two large tourism crowdsourcing platforms, namely Expedia and TripAdvisor. Building historical trust-based models of raters, our method is implemented as an external module and integrated with the collaborative filter through a post-recommendation component. The inter-user trust profiling history, traceability and authenticity are ensured by blockchain, since these profiles are stored as a smart contract in a private Ethereum network. Our empirical evaluation with HotelExpedia and Tripadvisor has consistently shown the positive impact of blockchain-based profiling on the quality (measured as recall) and transparency (determined via explanations) of recommendations.

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