Search Results for author: Antonio Rios-Navarro

Found 4 papers, 0 papers with code

Wide & Deep neural network model for patch aggregation in CNN-based prostate cancer detection systems

no code implementations20 May 2021 Lourdes Duran-Lopez, Juan P. Dominguez-Morales, Daniel Gutierrez-Galan, Antonio Rios-Navarro, Angel Jimenez-Fernandez, Saturnino Vicente-Diaz, Alejandro Linares-Barranco

Prostate cancer (PCa) is one of the most commonly diagnosed cancer and one of the leading causes of death among men, with almost 1. 41 million new cases and around 375, 000 deaths in 2020.

whole slide images

EdgeDRNN: Enabling Low-latency Recurrent Neural Network Edge Inference

no code implementations22 Dec 2019 Chang Gao, Antonio Rios-Navarro, Xi Chen, Tobi Delbruck, Shih-Chii Liu

This paper presents a Gated Recurrent Unit (GRU) based recurrent neural network (RNN) accelerator called EdgeDRNN designed for portable edge computing.

Edge-computing

Dynamic Vision Sensor integration on FPGA-based CNN accelerators for high-speed visual classification

no code implementations17 May 2019 Alejandro Linares-Barranco, Antonio Rios-Navarro, Ricardo Tapiador-Morales, Tobi Delbruck

The use of dynamic vision sensors (DVS) that emulate the behavior of a biological retina is taking an incremental importance to improve this applications due to its nature, where the information is represented by a continuous stream of spikes and the frames to be processed by the CNN are constructed collecting a fixed number of these spikes (called events).

General Classification

NullHop: A Flexible Convolutional Neural Network Accelerator Based on Sparse Representations of Feature Maps

no code implementations5 Jun 2017 Alessandro Aimar, Hesham Mostafa, Enrico Calabrese, Antonio Rios-Navarro, Ricardo Tapiador-Morales, Iulia-Alexandra Lungu, Moritz B. Milde, Federico Corradi, Alejandro Linares-Barranco, Shih-Chii Liu, Tobi Delbruck

By exploiting sparsity, NullHop achieves an efficiency of 368%, maintains over 98% utilization of the MAC units, and achieves a power efficiency of over 3TOp/s/W in a core area of 6. 3mm$^2$.

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