Search Results for author: Bogdan Penkovsky

Found 5 papers, 1 papers with code

Photonic Quantum Computing For Polymer Classification

no code implementations22 Nov 2022 Alexandrina Stoyanova, Taha Hammadia, Arno Ricou, Bogdan Penkovsky

The models obtained using either of the three hybrid methods successfully classified the VIS and NIR polymers.

Binary Classification Classification

In-Memory Resistive RAM Implementation of Binarized Neural Networks for Medical Applications

no code implementations20 Jun 2020 Bogdan Penkovsky, Marc Bocquet, Tifenn Hirtzlin, Jacques-Olivier Klein, Etienne Nowak, Elisa Vianello, Jean-Michel Portal, Damien Querlioz

With new memory technology available, emerging Binarized Neural Networks (BNNs) are promising to reduce the energy impact of the forthcoming machine learning hardware generation, enabling machine learning on the edge devices and avoiding data transfer over the network.

BIG-bench Machine Learning

Implementing Binarized Neural Networks with Magnetoresistive RAM without Error Correction

no code implementations12 Aug 2019 Tifenn Hirtzlin, Bogdan Penkovsky, Jacques-Olivier Klein, Nicolas Locatelli, Adrien F. Vincent, Marc Bocquet, Jean-Michel Portal, Damien Querlioz

One of the most exciting applications of Spin Torque Magnetoresistive Random Access Memory (ST-MRAM) is the in-memory implementation of deep neural networks, which could allow improving the energy efficiency of Artificial Intelligence by orders of magnitude with regards to its implementation on computers and graphics cards.

Stochastic Computing for Hardware Implementation of Binarized Neural Networks

1 code implementation3 Jun 2019 Tifenn Hirtzlin, Bogdan Penkovsky, Marc Bocquet, Jacques-Olivier Klein, Jean-Michel Portal, Damien Querlioz

In this work, we propose a stochastic computing version of Binarized Neural Networks, where the input is also binarized.

Emerging Technologies

Efficient Design of Hardware-Enabled Reservoir Computing in FPGAs

no code implementations4 May 2018 Bogdan Penkovsky, Laurent Larger, Daniel Brunner

In this work, we propose a new approach towards the efficient optimization and implementation of reservoir computing hardware reducing the required domain expert knowledge and optimization effort.

Dimensionality Reduction

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