Impact on Delay, Power and Area of Machine Learning-based Approximate Logic Synthesis
Created by W.Langdon from
gp-bibliography.bib Revision:1.8344
- @InProceedings{Prats-Ramos:2024:SBCCI,
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author = "Joao Carlos {Prats Ramos} and Naiara Sachetti and
Augusto Berndt and Jonata T. Carvalho and
Cristina Meinhardt",
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title = "Impact on Delay, Power and Area of Machine
Learning-based Approximate Logic Synthesis",
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booktitle = "2024 37th SBC/SBMicro/IEEE Symposium on Integrated
Circuits and Systems Design (SBCCI)",
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year = "2024",
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month = sep,
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keywords = "genetic algorithms, genetic programming, Cartesian
Genetic Programming, Integrated circuits, Measurement,
Accuracy, Libraries, Energy efficiency, Delays, Logic,
Optimisation, Standards, Logical Synthesis, Physical
Synthesis",
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DOI = "
doi:10.1109/SBCCI62366.2024.10703989",
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abstract = "Logical optimisation is a crucial step in circuit
synthesis, directly impacting the design's area, power,
and delay metrics. Machine Learning techniques are
particularly suitable for approximate logic synthesis
and optimisation in error-resilient scenarios. This
work investigates the impact of using ML techniques for
logic optimisation and enhancing energy efficiency in
approximate circuit synthesis. We consider three
state-of-the-art approximate logic optimisation
approaches based on mixed-ML, Decision Trees (DT), and
Cartesian Genetic Programming (CGP). This work presents
a comparative analysis of physical synthesis results
for a set of approximate circuit benchmarks, using an
open synthesis flow and a 45 nm technology standard
cell library. From the techniques evaluated, CGP-based
logical optimisation shows a reduction in area, power,
and delay of more than 50percent on average compared to
the mixed-ML approach. However, this improvement was
accompanied by an average accuracy loss of about
5percent. These findings highlight the substantial
potential of the CGP approach in optimising approximate
circuits.",
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notes = "Also known as \cite{10703989}",
- }
Genetic Programming entries for
Joao Carlos Prats Ramos
Naiara Sachetti
Augusto Andre Souza Berndt
Jonata Tyska Carvalho
Cristina Meinhardt
Citations