Vojtech Mrazek | DeepAI

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Muhammad Shafique
112 publications
Muhammad Abdullah Hanif
45 publications
Alberto Marchisio
29 publications
Maurizio Martina
21 publications
Tomas Vojnar
18 publications
Milan Ceska
14 publications
Bharath Srinivas Prabakaran
12 publications
Lukas Sekanina
11 publications
Zdenek Vasicek
9 publications
Beatrice Bussolino
7 publications
Andrea Massa
3 publications

∙ 03/08/2023

autoXFPGAs: An End-to-End Automated Exploration Framework for Approximate Accelerators in FPGA-Based Systems

Generation and exploration of approximate circuits and accelerators has ...

0 Bharath Srinivas Prabakaran, et al. ∙

∙ 10/11/2022

RoHNAS: A Neural Architecture Search Framework with Conjoint Optimization for Adversarial Robustness and Hardware Efficiency of Convolutional and Capsule Networks

Neural Architecture Search (NAS) algorithms aim at finding efficient Dee...

0 Alberto Marchisio, et al. ∙

∙ 06/27/2022

Designing Approximate Arithmetic Circuits with Combined Error Constraints

Approximate circuits trading the power consumption for the quality of re...

0 Milan Ceska, et al. ∙

∙ 05/06/2022

Optimization of BDD-based Approximation Error Metrics Calculations

Software methods introduced for automated design of approximate implemen...

0 Vojtech Mrazek, et al. ∙

∙ 03/09/2022

ArithsGen: Arithmetic Circuit Generator for Hardware Accelerators

Generators of arithmetic circuits can automatically deliver various impl...

0 Jan Klhufek, et al. ∙

∙ 01/28/2021

Evolutionary Neural Architecture Search Supporting Approximate Multipliers

There is a growing interest in automated neural architecture search (NAS...

0 Michal Pinos, et al. ∙

∙ 10/12/2020

DESCNet: Developing Efficient Scratchpad Memories for Capsule Network Hardware

Deep Neural Networks (DNNs) have been established as the state-of-the-ar...

0 Alberto Marchisio, et al. ∙

∙ 08/19/2020

NASCaps: A Framework for Neural Architecture Search to Optimize the Accuracy and Hardware Efficiency of Convolutional Capsule Networks

Deep Neural Networks (DNNs) have made significant improvements to reach ...

0 Alberto Marchisio, et al. ∙

∙ 04/23/2020

Semantically-Oriented Mutation Operator in Cartesian Genetic Programming for Evolutionary Circuit Design

Despite many successful applications, Cartesian Genetic Programming (CGP...

0 David Hodan, et al. ∙

∙ 04/22/2020

ApproxFPGAs: Embracing ASIC-Based Approximate Arithmetic Components for FPGA-Based Systems

There has been abundant research on the development of Approximate Circu...

0 Bharath Srinivas Prabakaran, et al. ∙

∙ 04/22/2020

Using Libraries of Approximate Circuits in Design of Hardware Accelerators of Deep Neural Networks

Approximate circuits have been developed to provide good tradeoffs betwe...

0 Vojtech Mrazek, et al. ∙

∙ 03/05/2020

Adaptive Verifiability-Driven Strategy for Evolutionary Approximation of Arithmetic Circuits

We present a novel approach for designing complex approximate arithmetic...

0 Milan Ceska, et al. ∙

∙ 02/21/2020

TFApprox: Towards a Fast Emulation of DNN Approximate Hardware Accelerators on GPU

Energy efficiency of hardware accelerators of deep neural networks (DNN)...

0 Filip Vaverka, et al. ∙

∙ 12/02/2019

ReD-CaNe: A Systematic Methodology for Resilience Analysis and Design of Capsule Networks under Approximations

Recent advances in Capsule Networks (CapsNets) have shown their superior...

0 Alberto Marchisio, et al. ∙

∙ 06/11/2019

ALWANN: Automatic Layer-Wise Approximation of Deep Neural Network Accelerators without Retraining

The state-of-the-art approaches employ approximate computing to improve ...

0 Vojtech Mrazek, et al. ∙

∙ 03/11/2019

Automated Circuit Approximation Method Driven by Data Distribution

We propose an application-tailored data-driven fully automated method fo...

0 Zdenek Vasicek, et al. ∙

∙ 02/22/2019

autoAx: An Automatic Design Space Exploration and Circuit Building Methodology utilizing Libraries of Approximate Components

Approximate computing is an emerging paradigm for developing highly ener...

0 Vojtech Mrazek, et al. ∙

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