3 citations · 3 across the 2 of their papers we have counts for
4 papers
Stochastic Layer-Wise Precision in Deep Neural Networks
Griffin Lacey, Graham W. Taylor, Shawki Areibi
Low precision weights, activations, and gradients have been proposed as a way to improve the computational efficiency and memory footprint of deep neural networks. Recently, low pr…
An Efficient Framework for Floor-plan Prediction of Dynamic Runtime Reconfigurable Systems
A. Al-Wattar, S. Areibi, G. Grewal
Several embedded application domains for reconfigurable systems tend to combine frequent changes with high performance demands of their workloads such as image processing, wearable…
Caffeinated FPGAs: FPGA Framework For Convolutional Neural Networks
Roberto DiCecco, Griffin Lacey, Jasmina Vasiljevic +3
Convolutional Neural Networks (CNNs) have gained significant traction in the field of machine learning, particularly due to their high accuracy in visual recognition. Recent works…
Deep Learning on FPGAs: Past, Present, and Future
Griffin Lacey, Graham W. Taylor, Shawki Areibi
The rapid growth of data size and accessibility in recent years has instigated a shift of philosophy in algorithm design for artificial intelligence. Instead of engineering algorit…