Machine Learning (ML) has demonstrated great promise in various fields, eg, self-driving,
smart city, which are fundamentally altering the way individuals and organizations live, work …

Serverless computing is a new pay-per-use cloud service paradigm that automates resource
scaling for stateless functions and can potentially facilitate bursty machine learning serving …

Recent deep learning models have moved beyond low dimensional regular grids such as
image, video, and speech, to high-dimensional graph-structured data, such as social …

Dynamic neural networks (NNs), which can adapt sparsely activated sub-networks to inputs
during inference, have shown significant advantages over static ones in terms of accuracy …

Modern deep neural networks increasingly make use of features such as control flow,
dynamic data structures, and dynamic tensor shapes. Existing deep learning systems focus …

Neural network (NN) processors are specially designed to handle deep learning tasks by
utilizing multilayer artificial NNs. They have been demonstrated to be useful in broad …

On-device training is essential for neural networks (NNs) to continuously adapt to new
online data, but can be time-consuming due to the device's limited computing power. To …

Though many compilation and runtime systems have been developed for DNNs in recent
years, the focus has largely been on static DNNs. Dynamic DNNs, where tensor shapes and …

Dynamic neural network (DyNN) enables high computational efficiency and strong
representation capability. However, training DyNN can face a memory capacity problem …

Optimizing deep learning models is generally performed in two steps:(i) high-level graph
optimizations such as kernel fusion and (ii) low level kernel optimizations such as those …