428 citations · 4.5k across the 191 of their papers we have counts for
5 papers · 2 filters
Learning to infer: RL-based search for DNN primitive selection on Heterogeneous Embedded Systems
Miguel de Prado, Nuria Pazos, Luca Benini
Deep Learning is increasingly being adopted by industry for computer vision applications running on embedded devices. While Convolutional Neural Networks' accuracy has achieved a m…
Extended Bit-Plane Compression for Convolutional Neural Network Accelerators
Lukas Cavigelli, Luca Benini
After the tremendous success of convolutional neural networks in image classification, object detection, speech recognition, etc., there is now rising demand for deployment of thes…
CBinfer: Exploiting Frame-to-Frame Locality for Faster Convolutional Network Inference on Video Streams
Lukas Cavigelli, Luca Benini
The last few years have brought advances in computer vision at an amazing pace, grounded on new findings in deep neural network construction and training as well as the availabilit…
Efficient Image Dataset Classification Difficulty Estimation for Predicting Deep-Learning Accuracy
Florian Scheidegger, Roxana Istrate, Giovanni Mariani +3
In the deep-learning community new algorithms are published at an incredible pace. Therefore, solving an image classification problem for new datasets becomes a challenging task, a…
XNORBIN: A 95 TOp/s/W Hardware Accelerator for Binary Convolutional Neural Networks
Andrawes Al Bahou, Geethan Karunaratne, Renzo Andri +2
Deploying state-of-the-art CNNs requires power-hungry processors and off-chip memory. This precludes the implementation of CNNs in low-power embedded systems. Recent research shows…