Improving Digital Circuit Synthesis of Complex Functions using Binary Weighted Fitness and Variable Mutation Rate in Cartesian Genetic Programming

Prashanth, H C; Rao, Madhav

doi:10.5220/0011539000003332

Cited by 4 publications

(1 citation statement)

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“…However, most modifications aim to add more operative dimensions or shift execution to a different platform without addressing the speedup mechanism to arrive at fitter solutions. Previously, the authors explored and demonstrated the desired modifications to the existing CGP configuration, namely BwF and exponentially varying mutation rate (eVar), which can produce functionally correct solutions at a remarkably fast pace [48]. The advantages of the modifications are showcased for basic nonlinear power functions and are verified for usage in activation functions that are otherwise difficult to achieve.…”

Section: Proposed Methodsmentioning

confidence: 99%

Accelerated and Highly Correlated ASIC Synthesis of AI Hardware Subsystems Using CGP

Prashanth,

Rao

2024

IET Computers & Digital Techniques

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Unconventional functions, including activation functions and power functions, are extremely hard-to-realize primarily due to the difficulty in arriving at the hierarchical design. The hierarchical design allows the synthesis tool to map the functionality with that of standard cells employed through the regular ASIC synthesis flow. For conventional functions, the hierarchical design is structured and then supplied to the synthesis flow, whereas, for unconventional functions, the same method is not reliable, since the current synthesis method does not offer any design-space exploration scheme to arrive at an easy-to-realize design entity. The unconventional functions either take a long synthesis run-time or additional efforts are spent in restructuring the hierarchical design for the desired function to synthesizable ones. Cartesian genetic programing (CGP) allows to not only incorporate custom logic gates for synthesizing the hierarchical design but also aids in the design-space exploration for the targeted function through the custom gates. The CGP configuration evolves difficult-to-realize complex functions with multiple solutions, and filtering through desired Pareto-optimal requirements offers a unique hierarchical design. Incorporating CGP-derived hierarchical designs into the traditional synthesis flow is instrumental for implementing and evaluating higher-order designs comprising nonlinear functional constructs. Six activation functions and power functions that fall in the category of unconventional functions are realized by the CGP method using custom cells to demonstrate the capability. Further, the hierarchical design of these unconventional functions is flattened and compared with the same function that is directly synthesized using basic gates. The CGP-derived synthesis method reports 3× less synthesis time for realizing the complex functions at the hierarchical level compared to the synthesis using basic gate cells. Hardware characteristics and error metrics are also investigated for the CGP realized complex functions and are made freely available for further usage to the research and designers’ community.

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Section: Proposed Methodsmentioning

confidence: 99%