2022
DOI: 10.1109/tcsi.2021.3133931
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ML-PLAC: Multiplierless Piecewise Linear Approximation for Nonlinear Function Evaluation

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Cited by 11 publications
(12 citation statements)
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“…For example, QPA-Gn can be configured as QPA-G1 which is a PWL method; or QPA-G2, which is a PWQ method. The QPA-Mn means the multiplier corresponding to the highest power is replaced with a shiftadder like [18], and the QPA-Sn-m means replace π‘š multipliers in the polynomial calculation circuit with π‘š barrel shifters. As these three methods are all QPA-based, designers can make some simple modifications to change from one approximation method to another, such as changing from QPA-G1 to QPA-G2, or from QPA-M1 to QPA-S2-1, to adapt to more ASIC design requirements while consistently ensuring that each specific method they used is currently the most advanced.…”
Section: A Methodology Overviewmentioning
confidence: 99%
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“…For example, QPA-Gn can be configured as QPA-G1 which is a PWL method; or QPA-G2, which is a PWQ method. The QPA-Mn means the multiplier corresponding to the highest power is replaced with a shiftadder like [18], and the QPA-Sn-m means replace π‘š multipliers in the polynomial calculation circuit with π‘š barrel shifters. As these three methods are all QPA-based, designers can make some simple modifications to change from one approximation method to another, such as changing from QPA-G1 to QPA-G2, or from QPA-M1 to QPA-S2-1, to adapt to more ASIC design requirements while consistently ensuring that each specific method they used is currently the most advanced.…”
Section: A Methodology Overviewmentioning
confidence: 99%
“…When the bit width of the multiplier is small enough, the universal multiplier can be replaced by a specially designed shift-adder to achieve a higher calculation speed. The PWL version of this idea is first proposed in [18] and named ML-SQ. However, the ML-SQ method cannot be used in arbitrary-order approximation, so we improve the fitting part of the ML-SQ method and propose the QPA-Mn fitting algorithm.…”
Section: Qpa-mn Fitting Algorithmmentioning
confidence: 99%
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