Today's auto-tuners (eg, AutoTVM, Ansor) generate efficient tensor programs by navigating
a large search space to identify effective implementations, but they do so with opaque …

Deep learning models rely on highly optimized tensor libraries for efficient inference on
heterogeneous hardware. Current deep compilers typically predetermine layouts of tensors …

The requirement for deploying deep learning (DL) models efficiently has boosted the
research of DL compilers. Especially, the difficulty of generating optimized tensor programs …

Recent advances in HPC Cloud field has made multi-core high performance VM services
more accessible. Emerging Arm based HPC systems are also receiving more attention …

With the growing importance of deploying deep neural networks (DNNs), there are
increasing demands to improve both the efficiency and quality of tensor program …

Cloud datacenters capable of provisioning high performance Machine Learning-as-a-
Service (MLaaS) at reduced resource cost is achieved via auto-tuning: automated tensor …

Model Compilation as a Service (MCaaS) has emerged as critical Machine Learning (ML)
supply chain infrastructure. It provides large-scale model optimization for heteroge-neous …

Deep learning models rely on highly optimized tensor libraries for efficient inference on
heterogeneous hardware. Current deep compilers typically predetermine layouts of tensors …

With the rapid growth of deep learning models and deep learning-based applications, how
to accelerate the inference of deep neural networks, especially neural network operators …

The rapid advancement of computing technologies presents increasingly complex
challenges in software and hardware development. Traditional programming approaches …