Approximating Complex Arithmetic Circuits with Guaranteed Worst-Case Relative Error

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@InProceedings{Ceska:2019:EUROCAST,

• author = "Milan {Ceska jr.} and Milan Ceska and Jiri Matyas and Adam Pankuch and Tomas Vojnar",
• title = "Approximating Complex Arithmetic Circuits with Guaranteed Worst-Case Relative Error",
• booktitle = "International Conference on Computer Aided Systems Theory, EUROCAST 2019",
• year = "2019",
• editor = "Roberto Moreno-Diaz and Franz Pichler and Alexis Quesada-Arencibia",
• volume = "12013",
• series = "Lecture Notes in Computer Science",
• pages = "482--490",
• address = "Las Palmas de Gran Canaria, Spain",
• month = "17-22 " # feb,
• publisher = "Springer",
• keywords = "genetic algorithms, genetic programming, Cartesian genetic programming",
• isbn13 = "978-3-030-45092-2",
• DOI = "doi:10.1007/978-3-030-45093-9_58",
• abstract = "We present a novel method allowing one to approximate complex arithmetic circuits with formal guarantees on the worst-case relative error, abbreviated as WCRE. WCRE represents an important error metric relevant in many applications including, e.g., approximation of neural network HW architectures. The method integrates SAT-based error evaluation of approximate circuits into a verifiability-driven search algorithm based on Cartesian genetic programming. We implement the method in our framework ADAC that provides various techniques for automated design of arithmetic circuits. Our experimental evaluation shows that, in many cases, the method offers a superior scalability and allows us to construct, within a few hours, high-quality approximations (providing trade-offs between the WCRE and size) for circuits with up to 32-bit operands. As such, it significantly improves the capabilities of ADAC.",

}

Genetic Programming entries for Milan Ceska jr Milan Ceska Jiri Matyas Adam Pankuch Tomas Vojnar

Citations