Semantically-oriented mutation operator in cartesian genetic programming for evolutionary circuit design

Hodan, David; Mrázek, Vojtěch; Vašíček, Zdeněk

doi:10.1145/3377930.3390188

Cited by 11 publications

(3 citation statements)

References 24 publications

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“…Furthermore, a search-based strategy that adopted CGP to resynthesize circuits for FPGA system design was presented in [41]. Overall, CGP provides better tradeoff metrics between hardware design parameters and error metrics than traditional methods [38], but the synthesis runtime is exceptionally high for a large number of inputs and complex nonlinear functionalities [42]. Additionally, the increased use of machine learning and artificial intelligence techniques mandates higher-order computations in 32-or 64-bit data formats [43].…”

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%

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

Prashanth,

Rao

2024

IET Computers & Digital Techniques

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“…In addition to adders, we also include multipliers. The evolutionary design of multipliers represents probably the hardest problem due to the complexity of the multiplication itself (the multipliers consist of a sequence of adders reducing the partial products to a single output vector) [7].…”

Section: Multipliersmentioning

confidence: 99%

Towards a General Boolean Function Benchmark Suite

Kalkreuth¹,

Vašíček

Husa

et al. 2023

Proceedings of the Companion Conference on Genetic and Evolutionary Computation

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over a decade ago, the first comprehensive review on the state of benchmarking in Genetic Programming (GP) analyzed the mismatch between the problems that are used to test the performance of GP systems and real-world problems. Since then, several benchmark suites in major GP problem domains have been proposed over time, which were able to fill some of the major gaps. In the framework of the first review about the state of benchmarking in GP, logic synthesis was classified as one of the major GP problem domains. However, a diverse and accessible benchmark suite for logic synthesis is still missing in the field of GP. In this work, we take a first step towards a benchmark suite for logic synthesis that covers different types of Boolean functions that are commonly used for the evaluation of GP systems. We also present baseline results that have been obtained by former work and in our evaluation experiments by using Cartesian Genetic Programming.

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“…Some existing approaches proposed to address the issue of representation scalability are decomposition [1,2] and more efficient genetic operators [3]. Existing decomposition approaches break complex circuits down into evolvable sub-circuits either via inputs [2] or outputs [1] and merge them into a complete circuit using varying strategies.…”

Section: Introductionmentioning

confidence: 99%

Evolving Multi-Output Digital Circuits Using Multi-Genome Grammatical Evolution

et al. 2023

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Grammatical Evolution is a Genetic Programming variant which evolves problems in any arbitrary language that is BNF compliant. Since its inception, Grammatical Evolution has been used to solve real-world problems in different domains such as bio-informatics, architecture design, financial modelling, music, software testing, game artificial intelligence and parallel programming. Multi-output problems deal with predicting numerous output variables simultaneously, a notoriously difficult problem. We present a Multi-Genome Grammatical Evolution better suited for tackling multi-output problems, specifically digital circuits. The Multi-Genome consists of multiple genomes, each evolving a solution to a single unique output variable. Each genome is mapped to create its executable object. The mapping mechanism, genetic, selection, and replacement operators have been adapted to make them well-suited for the Multi-Genome representation and the implementation of a new wrapping operator. Additionally, custom grammar syntax rules and a cyclic dependency-checking algorithm have been presented to facilitate the evolution of inter-output dependencies which may exist in multi-output problems. Multi-Genome Grammatical Evolution is tested on combinational digital circuit benchmark problems. Results show Multi-Genome Grammatical Evolution performs significantly better than standard Grammatical Evolution on these benchmark problems.

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Semantically-oriented mutation operator in cartesian genetic programming for evolutionary circuit design

Cited by 11 publications

References 24 publications

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

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

Towards a General Boolean Function Benchmark Suite

Evolving Multi-Output Digital Circuits Using Multi-Genome Grammatical Evolution

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