Transformer-based large language models (LLMs) demonstrate impressive performance
across various natural language processing tasks. Serving LLM inference for generating …

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 …

In the last three years, the largest dense deep learning models have grown over 1000x to
reach hundreds of billions of parameters, while the GPU memory has only grown by 5x (16 …

Large-scale model training has been a playing ground for a limited few requiring complex
model refactoring and access to prohibitively expensive GPU clusters. ZeRO-Offload …

With the growing model size of deep neural networks (DNN), deep learning training is
increasingly relying on handcrafted search spaces to find efficient parallelization execution …

Abstract Human-Agent Interaction is at the forefront of rapid development, with integrating
deep learning techniques into natural language processing representing significant …

Efficiently scheduling deep learning jobs on large-scale GPU clusters is crucial for job
performance, system throughput, and hardware utilization. It is getting ever more …

Fine-tuning models on edge devices like mobile phones would enable privacy-preserving
personalization over sensitive data. However, edge training has historically been limited to …

On-device learning is a promising technique for emerging privacy-preserving machine
learning paradigms. However, through quantitative experiments, we find that commodity …

Pipeline parallelism is a key technique for training large language models within GPU
clusters. However, it often leads to a memory imbalance problem, where certain GPUs face …