6 citations · 11 across the 7 of their papers we have counts for
8 papers
Multi-Objective Coevolution of Prompts and Templates for Circuit Approximation
Martin Tomasovic, Lukas Sekanina
Approximate multipliers deliberately relax computational accuracy to achieve gains in power efficiency, latency, and silicon area, which makes them well-suited for error-resilient…
Genetic Programming with Transformer-Based Mutation for Approximate Circuit Design
Ondrej Galeta, Lukas Sekanina
A recent trend is to leverage machine learning models to improve the evolutionary design and optimization process. We propose a novel transformer-based mutation operator for Cartes…
ObfAx: Obfuscation and IP Piracy Detection in Approximate Circuits
Lukas Sekanina, Vojtech Mrazek
Approximate circuits often achieve exceptional trade-offs between computational accuracy and hardware efficiency, making them attractive for deployment as reusable Intellectual Pro…
TRAPTI: Time-Resolved Analysis for SRAM Banking and Power Gating Optimization in Embedded Transformer Inference
Jan Klhufek, Alberto Marchisio, Vojtech Mrazek +2
Transformer neural networks achieve state-of-the-art accuracy across language and vision tasks, but their deployment on embedded hardware is hindered by stringent area, latency, an…
Evolutionary Multi-Objective Fusion of Deepfake Speech Detectors
Vojtěch Staněk, Martin Perešíni, Lukáš Sekanina +2
While deepfake speech detectors built on large self-supervised learning (SSL) models achieve high accuracy, employing standard ensemble fusion to further enhance robustness often r…
Late Breaking Result: FPGA-Based Emulation and Fault Injection for CNN Inference Accelerators
Filip Masar, Vojtech Mrazek, Lukas Sekanina
A new field programmable gate array (FPGA)-based emulation platform is proposed to accelerate fault tolerance analysis of inference accelerators of convolutional neural networks (C…
ApproxDARTS: Differentiable Neural Architecture Search with Approximate Multipliers
Michal Pinos, Lukas Sekanina, Vojtech Mrazek
Integrating the principles of approximate computing into the design of hardware-aware deep neural networks (DNN) has led to DNNs implementations showing good output quality and hig…
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…