FASCINET: A Fully Automated Single-Board Computer Generator Using Neural Networks

• Published in: IEEE transactions on computer-aided design of integrated circuits and systems 2022-12, Vol.41 (12), p.5435-5448
• Main Authors: Fayazi, Morteza, Colter, Zachary, Youbi, Zineb Benameur-El, Bagherzadeh, Javad, Ajayi, Tutu, Dreslinski, Ronald
• Format: Article
• Language: English
• Subjects: Automatic peripheral circuit generator tool; Automation; category recognition; Circuit design; commercial off-the-shelf database (COTS DB); Computer architecture; Data mining; datasheet; Design; Model accuracy; neural network (NN); Neural networks; single-board computer (SBC); table extraction; Tables (data)
• ISSN: 0278-0070; 1937-4151
• DOI: 10.1109/TCAD.2022.3158073

Designing single-board computers (SBCs) is becoming more challenging given the growing number of discrete components that are made available and the rate at which this number grows. Keeping track of all available components options, revisions, and functionalities is challenging for SBC designers who are striving for faster design cycles. Moreover, the procedure of deciding peripheral components, their values, and connections of an SBC is not only difficult because of various parameters that need to be considered but also is time consuming as there exist numerous components on a typical SBC nowadays. In this article, an SBC generator tool, FASCINET, is presented that uses a neural network (NN) model to design customized peripheral circuits for SBCs. The tool creates a large commercial off-the-shelf database (COTS DB) of existing components, efficiently searches through them, and selects optimal components for both main and peripheral components based on the user's requirements. Creating such a broad COTS DB requires processing abundant datasheets. A manual approach is time consuming, even if only a fraction of all available datasheets is considered. In order to automate this process, this article describes a novel NN-based approach for automatically categorizing datasheets and proposes an extraction technique for parsing relevant functional information from tables within. Our evaluation using a test set that contains over 770 000 components shows that the category of datasheets is identified correctly over 95% of the time. Additionally, the table extractor has a precision above 96%. Our proposed fully autonomous SBC design approach reduces the time for generating the schematic of an SBC to as little as 2 min. For validating the accuracy of our model, the netlists of 400 SBCs designed by FASCINET are compared to the human-designed versions. This evaluation shows that FASCINET is able to design SBCs that are identical to the manually designed ones except for minor differences.