129 citations · 462 across the 26 of their papers we have counts for
14 papers · 1 filter
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…
ApproxGNN: A Pretrained GNN for Parameter Prediction in Design Space Exploration for Approximate Computing
Ondrej Vlcek, Vojtech Mrazek
Approximate computing offers promising energy efficiency benefits for error-tolerant applications, but discovering optimal approximations requires extensive design space exploratio…
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…
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…
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…
Designing Approximate Arithmetic Circuits with Combined Error Constraints
Milan Češka, Jiří Matyáš, Vojtech Mrazek +1
Approximate circuits trading the power consumption for the quality of results play a key role in the development of energy-aware systems. Designing complex approximate circuits is,…
Optimization of BDD-based Approximation Error Metrics Calculations
Vojtech Mrazek
Software methods introduced for automated design of approximate implementations of arithmetic circuits rely on fast and accurate evaluation of approximate candidate implementations…
ArithsGen: Arithmetic Circuit Generator for Hardware Accelerators
Jan Klhufek, Vojtech Mrazek
Generators of arithmetic circuits can automatically deliver various implementations of arithmetic circuits that show different tradeoffs between the key circuit parameters (delay,…
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…
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…