129 citations · 334 across the 11 of their papers we have counts for
5 papers · 1 filter
Evolutionary Algorithms in Approximate Computing: A Survey
Lukas Sekanina
In recent years, many design automation methods have been developed to routinely create approximate implementations of circuits and programs that show excellent trade-offs between…
Evolutionary Neural Architecture Search Supporting Approximate Multipliers
Michal Pinos, Vojtech Mrazek, Lukas Sekanina
There is a growing interest in automated neural architecture search (NAS) methods. They are employed to routinely deliver high-quality neural network architectures for various chal…
Adaptive Verifiability-Driven Strategy for Evolutionary Approximation of Arithmetic Circuits
Milan Ceska, Jiri Matyas, Vojtech Mrazek +3
We present a novel approach for designing complex approximate arithmetic circuits that trade correctness for power consumption and play important role in many energy-aware applicat…
Optimizing Convolutional Neural Networks for Embedded Systems by Means of Neuroevolution
Filip Badan, Lukas Sekanina
Automated design methods for convolutional neural networks (CNNs) have recently been developed in order to increase the design productivity. We propose a neuroevolution method capa…
ALWANN: Automatic Layer-Wise Approximation of Deep Neural Network Accelerators without Retraining
Vojtech Mrazek, Zdenek Vasicek, Lukas Sekanina +2
The state-of-the-art approaches employ approximate computing to reduce the energy consumption of DNN hardware. Approximate DNNs then require extensive retraining afterwards to reco…