Key Points
1. The paper discusses the significance of large foundation models, such as Large Language Models (LLMs), Vision Transformers (ViTs), diffusion, and LLM-based multimodal models, in revolutionizing the machine learning lifecycle, from training to deployment, and highlights the substantial advancements in versatility and performance these models offer.
2. It emphasizes the considerable focus on developing resource-efficient strategies to support the growth of these large models in a scalable and environmentally sustainable way, examining both algorithmic and systemic aspects.
3. The success of large foundation models is rooted in their scalability, where their accuracy and generalization abilities can continuously expand with more data or parameters without altering the underlying simple algorithms and architectures, making them a cornerstone in the journey towards artificial general intelligence (AGI).
4. However, the scalability comes at the cost of huge resource demand, including computing processors like GPUs and TPUs, memory, energy, and network bandwidth. The paper provides evidence regarding the substantial electricity consumption by large foundation models and the associated carbon emissions.
5. The paper discusses how the huge resource footprint of large foundation models hinder their democratization, revealing that only a few major players are currently capable of training and deploying these models, allowing them powerful control over the public.
6. The survey aims to delve into the critical importance of research efforts aimed at enhancing the efficiency of these foundation models, exploring diverse strategies employed to make foundation models more resource-efficient, spanning from clouds to edge and devices.
7. The paper outlines the scope and rationales of the survey, including the exclusion of a significant body of work at hardware design and focusing mainly on physical resources, like computing, memory, storage, bandwidth, etc.
8. It highlights the organization of the survey, providing an overview of the structure and the organization of the core content.
9. The paper emphasizes the importance of efficient resource-efficient algorithms at the algorithm level, categorizing resource-efficient algorithms into pre-training, fine-tuning, serving algorithms, and model compression for large foundation models.
Summary
Transition to Foundation Models in Artificial Intelligence
The research paper explores the transition in the field of artificial intelligence from specialized deep learning models to versatile, one-size-fits-all foundation models, with a focus on large language models, vision transformers, and multimodal models. It highlights the challenges posed by the resource-hungry nature of these foundation models and emphasizes the critical importance of developing resource-efficient strategies. The paper delves into various strategies employed in research to enhance the efficiency of these foundation models, including algorithmic and systemic aspects, model compression, and training data deduction. Furthermore, it discusses innovations in algorithmic efficiency, system optimizations, data management techniques, and the development of novel architectures that are less resource-intensive. The research encompasses a wide array of topics, providing insights gleaned from existing literature and offering valuable perspectives on the future of resource-efficient algorithms and systems in the realm of foundation models.
Scalability and Efficiency of Foundation Models
The research paper explores the transition in the field of artificial intelligence from specialized deep learning models to versatile, one-size-fits-all foundation models. It investigates the scalability of these foundation models and the challenges posed by their resource-hungry nature. The paper explores efforts and strategies employed in research to enhance the efficiency of these foundation models while maintaining performance.
Techniques for Enhancing Resource Efficiency
The research paper delves into various techniques and methods aimed at making foundation models more resource-efficient. These techniques include masked autoencoders, patch-level alignment strategies, patch dropout, neural architecture search, and progressive learning strategies. The paper also discusses the use of knowledge distillation and quantization approaches to reduce the size and computational demands of these large models. Additionally, the paper explores the application of low-rank decomposition to approximate weight matrices in large foundation models.
Optimization of Distributed Training Systems and Frameworks
The paper categorizes techniques for optimizing distributed training systems, covering aspects such as resilience, parallelism, communication, storage, and heterogeneous GPUs. It also delves into the optimization of federated learning, serving in the cloud, and serving at the edge. The research paper provides an overview of resource-efficient systems and frameworks, including distributed training systems, federated learning techniques, compression methods, and serving systems for large foundational models.
Overall, the paper comprehensively discusses the various approaches and strategies employed in the research to enhance the efficiency and resource management of large foundation models in the field of artificial intelligence.
Reference: https://arxiv.org/abs/2401.08092v1