108 citations · 234 across the 37 of their papers we have counts for
3 papers · 1 filter
Caffe Barista: Brewing Caffe with FPGAs in the Training Loop
Diederik Adriaan Vink, Aditya Rajagopal, Stylianos I. Venieris +1
As the complexity of deep learning (DL) models increases, their compute requirements increase accordingly. Deploying a Convolutional Neural Network (CNN) involves two phases: train…
Multi-Precision Policy Enforced Training (MuPPET): A precision-switching strategy for quantised fixed-point training of CNNs
Aditya Rajagopal, Diederik Adriaan Vink, Stylianos I. Venieris +1
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…
Now that I can see, I can improve: Enabling data-driven finetuning of CNNs on the edge
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 t…