no code implementations • ICML 2020 • Aditya Rajagopal, Diederik Vink, Stylianos Venieris, Christos-Savvas Bouganis
Large-scale convolutional neural networks (CNNs) suffer from very long training times, spanning from hours to weeks, limiting the productivity and experimentation of deep learning practitioners.
no code implementations • 1 Mar 2022 • Aditya Rajagopal, Christos-Savvas Bouganis
Consequently, it is likely that the observed data distribution upon deployment is a subset of the training data distribution.
1 code implementation • 12 Aug 2021 • Aditya Rajagopal, Christos-Savvas Bouganis
The increased memory and processing capabilities of today's edge devices create opportunities for greater edge intelligence.
1 code implementation • 18 Jun 2020 • Diederik Adriaan Vink, Aditya Rajagopal, Stylianos I. Venieris, Christos-Savvas Bouganis
CNN training on FPGAs is a nascent field of research.
no code implementations • 16 Jun 2020 • Aditya Rajagopal, Diederik Adriaan Vink, Stylianos I. Venieris, Christos-Savvas Bouganis
Large-scale convolutional neural networks (CNNs) suffer from very long training times, spanning from hours to weeks, limiting the productivity and experimentation of deep learning practitioners.
1 code implementation • 15 Jun 2020 • Aditya Rajagopal, Christos-Savvas Bouganis
In today's world, a vast amount of data is being generated by edge devices that can be used as valuable training data to improve the performance of machine learning algorithms in terms of the achieved accuracy or to reduce the compute requirements of the model.