activity
20192025
most citedALWANN: Automatic Layer-Wise Approximation of Deep Neural Network Accelerators without Retraining

129 citations · 340 across the 14 of their papers we have counts for

collaborators
Showing 2020Show all

5 papers · 1 filter

cs.NE2020★ 16 cited

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…

cs.AR2020★ 32 cited

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…

cs.AR2020★ 12 cited

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…

cs.NE2020★ 5 cited

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…

cs.DC2020★ 40 cited

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…