Despite recent advances in tensor compilers, it often costs hours to generate an efficient
kernel for an operator, a compute-intensive sub-task in a deep neural network (DNN), on …

Large Language Models (LLMs) such as GPTs and LLaMa have ushered in a revolution in
machine intelligence, owing to their exceptional capabilities in a wide range of machine …

Real-time depth estimation is critical for the increasingly popular augmented reality and
virtual reality applications on mobile devices. Yet existing solutions are insufficient as they …

On-device Deep Neural Network (DNN) inference consumes significant computing
resources and development efforts. To alleviate that, we propose LUT-NN, the first system to …

For mobile devices, it is compelling to run sensitive GPU computation within a TrustZone
trusted execution environment (TEE). To minimize GPU software deployed in TEE, the …

The rapid advancement of edge artificial intelligence (AI) can be attributed to the widespread
use of edge consumer devices and the enhancement in System-on-Chip (SoC) capabilities …

Recent advancements in exploring machine learning models' dynamic spatial sparsity have
demonstrated great potential for superior efficiency and adaptability without compromising …

Web is increasingly becoming the primary platform to deliver AI services onto edge devices,
making in-browser deep learning (DL) inference more prominent. Nevertheless, the …

This work is motivated by recent developments in Deep Neural Networks, particularly the
Transformer architectures underlying applications such as ChatGPT, and the need for …

This paper introduces NLTSP, a deep learning-based cost model designed to optimize
tensor program performance in deep learning compilers. NLTSP, short for Nested Loop Tree …