Deep Neural Networks (DNNs) are becoming an important tool in modern computing
applications. Accelerating their training is a major challenge and techniques range from …

The rise of deep-learning (DL) has been fuelled by the improvements in accelerators. Due to
its unique features, the GPU continues to remain the most widely used accelerator for DL …

State-of-the-art neural network architectures such as ResNet, MobileNet, and DenseNet
have achieved outstanding accuracy over low MACs and small model size counterparts …

Existing deep neural network (DNN) frameworks optimize the computation graph of a DNN
by applying graph transformations manually designed by human experts. This approach …

Deep Neural Networks are becoming increasingly popular in always-on IoT edge devices
performing data analytics right at the source, reducing latency as well as energy …

Transformers are one of the most important machine learning workloads today. Training one
is a very compute-intensive task, often taking days or weeks, and significant attention has …

Ultrafast dynamic machine vision in the optical domain can provide unprecedented
perspectives for high-performance computing. However, owing to the limited degrees of …

Since neural networks renaissance, convolutional neural networks (ConvNets) have
demonstrated a state-of-the-art performance in several emerging artificial intelligence tasks …

In the realm of surface electromyography (sEMG) gesture recognition, deep learning
algorithms are seldom employed. This is due in part to the large quantity of data required for …

High-performance tensor programs are critical for efficiently deploying deep neural network
(DNN) models in real-world tasks. Existing frameworks optimize tensor programs by …