Article: Enhancing circuit adaptability in VLSI using hybrid optimisation for functional unit selection and resource allocation in high-level synthesis Journal: International Journal of Bio-Inspired Computation (IJBIC) 2025 Vol.25 No.4 pp.205 - 214 Abstract: Very-large-scale integration (VLSI) plays a crucial role in integrating transistors into a single chip, yet variations in process-voltage-temperature (PVT) present challenges for accuracy. In this manuscript, enhancing circuit adaptability in VLSI using hybrid optimisation for functional unit selection and resource allocation in high-level synthesis (VEA-Hyb-TSGTO-HLS) is proposed. It begins with data flow analysis (DFA), where the behavioural inputs of the VLSI circuits are analysed to gather crucial information about data flow and operation dependencies. Then volcano eruption algorithm (VEA) is used to determine the optimal functional unit based on variations in PVT, ensuring adaptability to changing conditions. Finally, hybrid transient search and group teaching optimisation algorithm (Hyb-TSGTO) is used to estimate the resource allocation. The proposed VEA-Hyb-TSGTO-HLS approach has achieved 24.6%, 21.4%, and 14.5% lesser cost and 15.6%, 18.8%, and 19.3% higher FU selection accuracy for using s38584 circuit when compared with the existing state of art methods. Inderscience Publishers - linking academia, business and industry through research

Title: Enhancing circuit adaptability in VLSI using hybrid optimisation for functional unit selection and resource allocation in high-level synthesis

Authors: M. Thillai Rani; K.P. Sai Pradeep; R. Sivakumar; S. Suresh Kumar

Addresses: Department of Electronics and Communication Engineering, Sri Krishna College of Technology, Coimbatore, Tamil Nadu, India ' Department of Electronics and Communication Engineering, Dr. N.G.P. Institute of Technology, Coimbatore, Tamil Nadu, India ' Sai Vortex Energy Solutions, Coimbatore, Tamil Nadu, India ' Department of Electronics and Communication Engineering, KGiSL Institute of Technology, Coimbatore, Tamil Nadu, India

Abstract: Very-large-scale integration (VLSI) plays a crucial role in integrating transistors into a single chip, yet variations in process-voltage-temperature (PVT) present challenges for accuracy. In this manuscript, enhancing circuit adaptability in VLSI using hybrid optimisation for functional unit selection and resource allocation in high-level synthesis (VEA-Hyb-TSGTO-HLS) is proposed. It begins with data flow analysis (DFA), where the behavioural inputs of the VLSI circuits are analysed to gather crucial information about data flow and operation dependencies. Then volcano eruption algorithm (VEA) is used to determine the optimal functional unit based on variations in PVT, ensuring adaptability to changing conditions. Finally, hybrid transient search and group teaching optimisation algorithm (Hyb-TSGTO) is used to estimate the resource allocation. The proposed VEA-Hyb-TSGTO-HLS approach has achieved 24.6%, 21.4%, and 14.5% lesser cost and 15.6%, 18.8%, and 19.3% higher FU selection accuracy for using s38584 circuit when compared with the existing state of art methods.

Keywords: group teaching optimisation; high level synthesis; HLS; transient search optimisation; TSO; volcano eruption algorithm; VEA; very-large-scale integration; VLSI.

DOI: 10.1504/IJBIC.2025.146918

International Journal of Bio-Inspired Computation, 2025 Vol.25 No.4, pp.205 - 214

Received: 06 Mar 2024
Accepted: 19 Oct 2024
Published online: 26 Jun 2025 *

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Title: Enhancing circuit adaptability in VLSI using hybrid optimisation for functional unit selection and resource allocation in high-level synthesis

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