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Found 4 papers, 1 papers with code
Stochastic Layer-Wise Precision in Deep Neural Networks
Low precision weights, activations, and gradients have been proposed as a way to improve the computational efficiency and memory footprint of deep neural networks.
Caffeinated FPGAs: FPGA Framework For Convolutional Neural Networks
Convolutional Neural Networks (CNNs) have gained significant traction in the field of machine learning, particularly due to their high accuracy in visual recognition.
Deep Learning on FPGAs: Past, Present, and Future
The rapid growth of data size and accessibility in recent years has instigated a shift of philosophy in algorithm design for artificial intelligence.
Learning Human Identity from Motion Patterns
We present a large-scale study exploring the capability of temporal deep neural networks to interpret natural human kinematics and introduce the first method for active biometric authentication with mobile inertial sensors.
