129 citations · 340 across the 14 of their papers we have counts for
14 papers
Arbitrary Precision Printed Ternary Neural Networks with Holistic Evolutionary Approximation
Vojtech Mrazek, Konstantinos Balaskas, Paula Carolina Lozano Duarte +3
Printed electronics offer a promising alternative for applications beyond silicon-based systems, requiring properties like flexibility, stretchability, conformality, and ultra-low…
TinyverseGP: Towards a Modular Cross-domain Benchmarking Framework for Genetic Programming
Roman Kalkreuth, Fabricio Olivetti de França, Julian Dierkes +4
Over the years, genetic programming (GP) has evolved, with many proposed variations, especially in how they represent a solution. Being essentially a program synthesis algorithm, i…
AxMED: Formal Analysis and Automated Design of Approximate Median Filters using BDDs
Vojtech Mrazek, Zdenek Vasicek
The increasing demand for energy-efficient solutions has led to the emergence of an approximate computing paradigm that enables power-efficient implementations in various applicati…
Evolutionary Approximation of Ternary Neurons for On-sensor Printed Neural Networks
Vojtech Mrazek, Argyris Kokkinis, Panagiotis Papanikolaou +5
Printed electronics offer ultra-low manufacturing costs and the potential for on-demand fabrication of flexible hardware. However, significant intrinsic constraints stemming from t…
Exploring Quantization and Mapping Synergy in Hardware-Aware Deep Neural Network Accelerators
Jan Klhufek, Miroslav Safar, Vojtech Mrazek +2
Energy efficiency and memory footprint of a convolutional neural network (CNN) implemented on a CNN inference accelerator depend on many factors, including a weight quantization st…
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…
Semantically-Oriented Mutation Operator in Cartesian Genetic Programming for Evolutionary Circuit Design
David Hodan, Vojtech Mrazek, Zdenek Vasicek
Despite many successful applications, Cartesian Genetic Programming (CGP) suffers from limited scalability, especially when used for evolutionary circuit design. Considering the mu…
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…
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…