Enlightened by the big success of pre-training in natural language processing, pre-trained
models for programming languages have been widely used to promote code intelligence in …

The exponential growth in data and parameters in modern neural networks has created the
need to distribute these models across multiple devices for efficient training, resulting in the …

The proliferation of Virtual and Augmented Reality (VR/AR) and the Internet of Things (IoT)
applications is driving the demand for efficient Deep Neural Network (DNN) inference at the …

Coping with the growing demand for data and parameters in complex neural network (NN)
models of contemporary times typically involves distributing them across multiple devices …

Deep neural network (DNN) foundation models are currently exhibiting high prediction
accuracy and strong adaptability to broad tasks with remarkably large model scales. They …

The escalating size of Deep Neural Networks (DNNs) has spurred a growing research
interest in hosting and serving DNN models across multiple devices. A number of studies …

The rapidly expanding neural network models are becoming increasingly challenging to run
on a single device. Hence, model parallelism over multiple devices is critical to …

Existing approaches for device placement ignore the topological features of computation
graphs and rely mostly on heuristic methods for graph partitioning. At the same time, they …

The accuracy of the neural networks can usually be improved by increasing the size of the
dataset and the layers or operators of the network, as it has strong composability. But, it …

The rapidly enlarging neural network models are becoming increasingly challenging to run
on a single device. Hence model parallelism over multiple devices is critical to guarantee …