129 citations · 334 across the 11 of their papers we have counts for
11 papers
Xel-FPGAs: An End-to-End Automated Exploration Framework for Approximate Accelerators in FPGA-Based Systems
Bharath Srinivas Prabakaran, Vojtech Mrazek, Zdenek Vasicek +2
Generation and exploration of approximate circuits and accelerators has been a prominent research domain to achieve energy-efficiency and/or performance improvements. This research…
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…
ApproxFPGAs: Embracing ASIC-Based Approximate Arithmetic Components for FPGA-Based Systems
Bharath Srinivas Prabakaran, Vojtech Mrazek, Zdenek Vasicek +2
There has been abundant research on the development of Approximate Circuits (ACs) for ASICs. However, previous studies have illustrated that ASIC-based ACs offer asymmetrical gains…
Using Libraries of Approximate Circuits in Design of Hardware Accelerators of Deep Neural Networks
Vojtech Mrazek, Lukas Sekanina, Zdenek Vasicek
Approximate circuits have been developed to provide good tradeoffs between power consumption and quality of service in error resilient applications such as hardware accelerators of…
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…
TFApprox: Towards a Fast Emulation of DNN Approximate Hardware Accelerators on GPU
Filip Vaverka, Vojtech Mrazek, Zdenek Vasicek +1
Energy efficiency of hardware accelerators of deep neural networks (DNN) can be improved by introducing approximate arithmetic circuits. In order to quantify the error introduced b…
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…
autoAx: An Automatic Design Space Exploration and Circuit Building Methodology utilizing Libraries of Approximate Components
Vojtech Mrazek, Muhammad Abdullah Hanif, Zdenek Vasicek +2
Approximate computing is an emerging paradigm for developing highly energy-efficient computing systems such as various accelerators. In the literature, many libraries of elementary…
Automated Circuit Approximation Method Driven by Data Distribution
Zdenek Vasicek, Vojtech Mrazek, Lukas Sekanina
We propose an application-tailored data-driven fully automated method for functional approximation of combinational circuits. We demonstrate how an application-level error metric s…