Zobrazeno 1 - 10
of 71
pro vyhledávání: '"Mrazek, Vojtech"'
Autor:
Mrazek, Vojtech, Kokkinis, Argyris, Papanikolaou, Panagiotis, Vasicek, Zdenek, Siozios, Kostas, Tzimpragos, Georgios, Tahoori, Mehdi, Zervakis, Georgios
Printed electronics offer ultra-low manufacturing costs and the potential for on-demand fabrication of flexible hardware. However, significant intrinsic constraints stemming from their large feature sizes and low integration density pose design chall
Externí odkaz:
http://arxiv.org/abs/2407.20589
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 strategy (i.e., data types and bit-widths) and mapping (i.e., placement
Externí odkaz:
http://arxiv.org/abs/2404.05368
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 highly optimized hardware parameters such as low latency or inference ene
Externí odkaz:
http://arxiv.org/abs/2404.08002
Autor:
Prabakaran, Bharath Srinivas, Mrazek, Vojtech, Vasicek, Zdenek, Sekanina, Lukas, Shafique, Muhammad
Generation and exploration of approximate circuits and accelerators has been a prominent research domain to achieve energy-efficiency and/or performance improvements. This research has predominantly focused on ASICs, while not achieving similar gains
Externí odkaz:
http://arxiv.org/abs/2303.04734
Autor:
Marchisio, Alberto, Mrazek, Vojtech, Massa, Andrea, Bussolino, Beatrice, Martina, Maurizio, Shafique, Muhammad
Neural Architecture Search (NAS) algorithms aim at finding efficient Deep Neural Network (DNN) architectures for a given application under given system constraints. DNNs are computationally-complex as well as vulnerable to adversarial attacks. In ord
Externí odkaz:
http://arxiv.org/abs/2210.05276
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, however, a very difficult and computationally demanding process. When
Externí odkaz:
http://arxiv.org/abs/2206.13077
Autor:
Mrazek, Vojtech
Software methods introduced for automated design of approximate implementations of arithmetic circuits rely on fast and accurate evaluation of approximate candidate implementations. To accelerate the evaluation of circuit error, we propose four novel
Externí odkaz:
http://arxiv.org/abs/2205.03267
Autor:
Klhufek, Jan, Mrazek, Vojtech
Generators of arithmetic circuits can automatically deliver various implementations of arithmetic circuits that show different tradeoffs between the key circuit parameters (delay, area, power consumption). However, existing (freely-)available generat
Externí odkaz:
http://arxiv.org/abs/2203.04649
There is a growing interest in automated neural architecture search (NAS) methods. They are employed to routinely deliver high-quality neural network architectures for various challenging data sets and reduce the designer's effort. The NAS methods ut
Externí odkaz:
http://arxiv.org/abs/2101.11883
Deep Neural Networks (DNNs) have been established as the state-of-the-art algorithm for advanced machine learning applications. Recently proposed by the Google Brain's team, the Capsule Networks (CapsNets) have improved the generalization ability, as
Externí odkaz:
http://arxiv.org/abs/2010.05754