Artificial Intelligence (AI) is currently experiencing a bloom driven by deep learning (DL)
techniques, which rely on networks of connected simple computing units operating in …

Successful integration of deep neural networks (DNNs) or deep learning (DL) has resulted
in breakthroughs in many areas. However, deploying these highly accurate models for data …

This paper introduces two extensions to the popular PyTorch machine learning framework,
TorchDynamo and TorchInductor, which implement the torch. compile feature released in …

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

Photonic computing enables faster and more energy-efficient processing of vision data,,,–.
However, experimental superiority of deployable systems remains a challenge because of …

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 …

In the last decade of automatic speech recognition (ASR) research, the introduction of deep
learning has brought considerable reductions in word error rate of more than 50% relative …

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

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