Matrix computation, as a fundamental building block of information processing in science
and technology, contributes most of the computational overheads in modern signal …

This paper explores the environmental impact of the super-linear growth trends for AI from a
holistic perspective, spanning Data, Algorithms, and System Hardware. We characterize the …

Transformers are slow and memory-hungry on long sequences, since the time and memory
complexity of self-attention are quadratic in sequence length. Approximate attention …

We investigate the optimal model size and number of tokens for training a transformer
language model under a given compute budget. We find that current large language models …

Though neural radiance fields (" NeRF") have demonstrated impressive view synthesis
results on objects and small bounded regions of space, they struggle on" unbounded" …

Abstract Neural Radiance Fields (NeRF) have emerged as a powerful representation for the
task of novel view synthesis due to their simplicity and state-of-the-art performance. Though …

We investigate the optimal model size and number of tokens for training a transformer
language model under a given compute budget. We find that current large language models …

Learning is highly important for edge intelligence devices to adapt to different application
scenes and owners. Current technologies for training neural networks require moving …

AI is undergoing a paradigm shift with the rise of models (eg, BERT, DALL-E, GPT-3) that are
trained on broad data at scale and are adaptable to a wide range of downstream tasks. We …

The advent of artificial intelligence (AI) has significantly impacted the traditional judicial
industry. Moreover, recently, with the development of the concept of AI-generated content …