Concept
Multiplieur
Publications associées (32)
DBFS: Dynamic Bitwidth-Frequency Scaling for Efficient Software-defined SIMD
Giovanni Ansaloni, Alexandre Sébastien Julien Levisse, Pengbo Yu, Flavio Ponzina
Machine learning algorithms such as Convolutional Neural Networks (CNNs) are characterized by high robustness towards quantization, supporting small-bitwidth fixed-point arithmetic at inference time with little to no degradation in accuracy. In turn, small ...
2024EdgeAI-Aware Design of In-Memory Computing Architectures
Driven by the demand for real-time processing and the need to minimize latency in AI algorithms, edge computing has experienced remarkable progress. Decision-making AI applications stand out for their heavy reliance on data-centric operations, predominantl ...
EPFL2024Multiplier Architectures: Challenges and Opportunities with Plasmonic-based Logic
Giovanni De Micheli, Mathias Soeken, Eleonora Testa, Odysseas Zografos
Emerging technologies such as plasmonics and photonics are promising alternatives to CMOS for high throughput applications, thanks to their waveguide's low power consumption and high speed of computation. Besides these qualities, these novel technologies a ...
2020Multiplier Architectures: Challenges and Opportunities with Plasmonic-based Logic
Giovanni De Micheli, Mathias Soeken, Eleonora Testa, Odysseas Zografos
Emerging technologies such as plasmonics and photonics are promising alternatives to CMOS for high throughput applications, thanks to their waveguide's low power consumption and high speed of computation. Besides these qualities, these novel technologies a ...
IEEE2020Review and Benchmarking of Precision-Scalable Multiply-Accumulate Unit Architectures for Embedded Neural-Network Processing
Christian Enz, Vincent Frédéric Camus
The current trend for deep learning has come with an enormous computational need for billions of Multiply-Accumulate (MAC) operations per inference. Fortunately, reduced precision has demonstrated large benefits with low impact on accuracy, paving the way ...
2019Survey of Precision-Scalable Multiply-Accumulate Units for Neural-Network Processing
Christian Enz, Vincent Frédéric Camus
The current trend for deep learning has come with an enormous computational need for billions of Multiply-Accumulate (MAC) operations per inference. Fortunately, reduced precision has demonstrated large benefits with low impact on accuracy, paving the way ...
IEEE2019Inner-Loop-Free ADMM for Cryo-EM
Michaël Unser, Laurène Donati, Emmanuel Emilien Louis Soubies
Thanks to recent advances in signal processing, the interest for fast ℓ1-regularized reconstruction algorithms in cryo-electron microscopy (cryo-EM) has intensified. The approaches based on the alternating-direction of multipliers method (ADMM) are particu ...
IEEE2019Adaptive simulation-based framework for error characterization of inexact circuits
To design faster and more energy-efficient systems, numerous inexact arithmetic operators have been proposed, generally obtained by modifying the logic structure of conventional circuits. However, as the quality of service of an application has to be ensur ...
PERGAMON-ELSEVIER SCIENCE LTD2019An Associativity-Agnostic in-Cache Computing Architecture Optimized for Multiplication
David Atienza Alonso, Marina Zapater Sancho, Alexandre Sébastien Julien Levisse, Marco Antonio Rios, William Andrew Simon
With the spread of cloud services and Internet of Things concept, there is a popularization of machine learning and artificial intelligence based analytics in our everyday life. However, an efficient deployment of these data-intensive services requires per ...
2019Design of approximate and precision-scalable circuits for embedded multimedia and neural-network processing
Density, speed and energy efficiency of integrated circuits have been increasing exponentially for the last four decades following Moore's law. However, power and reliability pose several challenges to the future of technology scaling. Approximate computin ...
EPFL2019