Enhanced chimp optimization algorithm for high level synthesis of digital filters.

Area of Science:

• * Electronic Design Automation
• * Computer Engineering
• * Algorithm Optimization

Background:

• * High-Level Synthesis (HLS) for digital filters is a complex optimization challenge in electronic design automation.
• * Existing methods for HLS often struggle with complex design spaces and can get trapped in local optima.
• * There is a continuous need for more efficient and effective approaches to digital filter design.

Purpose of the Study:

• * To develop a new, robust framework for the High-Level Synthesis of digital filters.
• * To enhance the optimization capabilities for digital circuit design.
• * To address the shortcomings of current HLS methodologies.

Main Methods:

• * A novel framework combining the Chimp Optimizer algorithm with sine cosine functions was developed.
• * The sine cosine phases were integrated to improve the exploitation capabilities of the Chimp Optimizer.
• * The proposed approach was designed to avoid local optima during the search for optimal design paths.

Main Results:

• * The algorithm was tested on 23 standard test suites and 14 digital filters.
• * Performance was evaluated against single and multi-objective functions, comparing metrics like score, maxima, average, standard deviation, execution time, area, and speed.
• * The proposed method demonstrated superior performance compared to two recent algorithms in HLS digital filter design.

Conclusions:

• * The proposed hybrid algorithm significantly outperforms existing methods in High-Level Synthesis for digital filters.
• * Key advantages include rapid runtime, reduced delay, minimized occupied area, and lower power consumption.
• * This approach offers substantial cost reduction and increased design speed for digital systems.