Control Variate Approximation for DNN Accelerators

In this work, we introduce a control variate approximation technique for low error approximate Deep Neural Network (DNN) accelerators. The control variate technique is used in Monte Carlo methods to achieve variance reduction. Our approach significantly decreases the induced error due to approximate...

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Vydané v:	2021 58th ACM/IEEE Design Automation Conference (DAC) s. 481 - 486
Hlavní autori:	Zervakis, Georgios, Spantidi, Ourania, Anagnostopoulos, Iraklis, Amrouch, Hussam, Henkel, Jorg
Médium:	Konferenčný príspevok..
Jazyk:	English
Vydavateľské údaje:	IEEE 05.12.2021
Predmet:	Approximate Computing Arithmetic Circuits Control Variate Convolution Deep learning Deep Neural Networks Design automation Low Power MAC Array Mathematical analysis Monte Carlo methods
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