The emerging field of artificial intelligence of things (AIoT, AI+ IoT) is driven by the
widespread use of intelligent infrastructures and the impressive success of deep learning …

With the increasing size of DNN models and the growing discrepancy between compute
performance and memory bandwidth, fusing multiple layers together to reduce off-chip …

Machine learning models with various tensor operators are becoming ubiquitous in recent
years. There are two types of operators in machine learning: compute-intensive operators …

With the growing demand for processing higher fidelity data and the use of faster computing
cores in newer hardware accelerators, modern deep neural networks (DNNs) are becoming …

The scaling up of deep neural networks has been demonstrated to be effective in improving
model quality, but also encompasses several training challenges in terms of training …

Abstract Modern Deep Neural Network (DNN) frameworks use tensor operators as the main
building blocks of DNNs. However, we observe that operator-based construction of DNNs …

Deep learning has been used in various domains, including Web services. Convolutional
neural networks (CNNs), which are deep learning representatives, are among the most …

Compiler optimization plays an increasingly important role to boost the performance of
machine learning models for data processing and management. With increasingly complex …

Dynamic shape computations have become critical in modern machine learning workloads,
especially in emerging large language models. The success of these models has driven …

Transformer has dominated the field of natural language processing because of its strong
capability in learning from sequential input data. In recent years, various computing and …