Improving the performance of deep neural networks (DNNs) is important to...
Compiler architects increasingly look to machine learning when building
...
Library migration is a challenging problem, where most existing approach...
When deploying a deep neural network on constrained hardware, it is poss...
Symbolic holes are one of the fundamental building blocks of solver-aide...
Convolutional Neural Networks (CNN) are becoming a common presence in ma...
Effective program synthesis requires a way to minimise the number of
can...
The desire to run neural networks on low-capacity edge devices has led t...
Despite recent developments, deploying deep neural networks on resource
...
The task of accelerating large neural networks on general purpose hardwa...
Pruning is a popular technique for compressing a neural network: a large...
Convolutional Neural Networks (CNNs) are extremely computationally deman...
SLAM is becoming a key component of robotics and augmented reality (AR)
...
In the last decade, machine learning based compilation has moved from an...