A Survey: Collaborative Hardware and Software Design in the Era of Large Language Models

• URL: http://arxiv.org/abs/2410.07265v1
• Date: Tue, 8 Oct 2024 21:46:52 GMT
• Title: A Survey: Collaborative Hardware and Software Design in the Era of Large Language Models
• Authors: Cong Guo, Feng Cheng, Zhixu Du, James Kiessling, Jonathan Ku, Shiyu Li, Ziru Li, Mingyuan Ma, Tergel Molom-Ochir, Benjamin Morris, Haoxuan Shan, Jingwei Sun, Yitu Wang, Chiyue Wei, Xueying Wu, Yuhao Wu, Hao Frank Yang, Jingyang Zhang, Junyao Zhang, Qilin Zheng, Guanglei Zhou, Hai, Li, Yiran Chen,
• Abstract summary: The rapid development of large language models (LLMs) has significantly transformed the field of artificial intelligence. These models are increasingly integrated into diverse applications, impacting both research and industry. This paper surveys hardware and software co-design approaches specifically tailored to address the unique characteristics and constraints of large language models.
• Score: 16.250856588632637
• License: http://creativecommons.org/licenses/by/4.0/
• Abstract: The rapid development of large language models (LLMs) has significantly transformed the field of artificial intelligence, demonstrating remarkable capabilities in natural language processing and moving towards multi-modal functionality. These models are increasingly integrated into diverse applications, impacting both research and industry. However, their development and deployment present substantial challenges, including the need for extensive computational resources, high energy consumption, and complex software optimizations. Unlike traditional deep learning systems, LLMs require unique optimization strategies for training and inference, focusing on system-level efficiency. This paper surveys hardware and software co-design approaches specifically tailored to address the unique characteristics and constraints of large language models. This survey analyzes the challenges and impacts of LLMs on hardware and algorithm research, exploring algorithm optimization, hardware design, and system-level innovations. It aims to provide a comprehensive understanding of the trade-offs and considerations in LLM-centric computing systems, guiding future advancements in AI. Finally, we summarize the existing efforts in this space and outline future directions toward realizing production-grade co-design methodologies for the next generation of large language models and AI systems.

Related papers

• On-Device Language Models: A Comprehensive Review [26.759861320845467]
  Review examines the challenges of deploying computationally expensive large language models on resource-constrained devices. Paper investigates on-device language models, their efficient architectures, as well as state-of-the-art compression techniques. Case studies of on-device language models from major mobile manufacturers demonstrate real-world applications and potential benefits.
  arXiv  Detail & Related papers  (2024-08-26T03:33:36Z)
• A Comprehensive Review of Multimodal Large Language Models: Performance and Challenges Across Different Tasks [74.52259252807191]
  Multimodal Large Language Models (MLLMs) address the complexities of real-world applications far beyond the capabilities of single-modality systems. This paper systematically sorts out the applications of MLLM in multimodal tasks such as natural language, vision, and audio.
  arXiv  Detail & Related papers  (2024-08-02T15:14:53Z)
• A Survey on Hardware Accelerators for Large Language Models [0.0]
  Large Language Models (LLMs) have emerged as powerful tools for natural language processing tasks. There is a pressing need to address the computational challenges associated with their scale and complexity.
  arXiv  Detail & Related papers  (2024-01-18T11:05:03Z)
• Machine Learning Insides OptVerse AI Solver: Design Principles and Applications [74.67495900436728]
  We present a comprehensive study on the integration of machine learning (ML) techniques into Huawei Cloud's OptVerse AI solver. We showcase our methods for generating complex SAT and MILP instances utilizing generative models that mirror multifaceted structures of real-world problem. We detail the incorporation of state-of-the-art parameter tuning algorithms which markedly elevate solver performance.
  arXiv  Detail & Related papers  (2024-01-11T15:02:15Z)
• Beyond Efficiency: A Systematic Survey of Resource-Efficient Large Language Models [33.50873478562128]
  Large Language Models (LLMs) bring forth challenges in the high consumption of computational, memory, energy, and financial resources. This survey aims to systematically address these challenges by reviewing a broad spectrum of techniques designed to enhance the resource efficiency of LLMs.
  arXiv  Detail & Related papers  (2024-01-01T01:12:42Z)
• Towards Efficient Generative Large Language Model Serving: A Survey from Algorithms to Systems [14.355768064425598]
  generative large language models (LLMs) stand at the forefront, revolutionizing how we interact with our data. However, the computational intensity and memory consumption of deploying these models present substantial challenges in terms of serving efficiency. This survey addresses the imperative need for efficient LLM serving methodologies from a machine learning system (MLSys) research perspective.
  arXiv  Detail & Related papers  (2023-12-23T11:57:53Z)
• The Efficiency Spectrum of Large Language Models: An Algorithmic Survey [54.19942426544731]
  The rapid growth of Large Language Models (LLMs) has been a driving force in transforming various domains. This paper examines the multi-faceted dimensions of efficiency essential for the end-to-end algorithmic development of LLMs.
  arXiv  Detail & Related papers  (2023-12-01T16:00:25Z)
• A Survey of Serverless Machine Learning Model Inference [0.0]
  Generative AI, Computer Vision, and Natural Language Processing have led to an increased integration of AI models into various products. This survey aims to summarize and categorize the emerging challenges and optimization opportunities for large-scale deep learning serving systems.
  arXiv  Detail & Related papers  (2023-11-22T18:46:05Z)
• Recommender Systems in the Era of Large Language Models (LLMs) [62.0129013439038]
  Large Language Models (LLMs) have revolutionized the fields of Natural Language Processing (NLP) and Artificial Intelligence (AI) We conduct a comprehensive review of LLM-empowered recommender systems from various aspects including Pre-training, Fine-tuning, and Prompting.
  arXiv  Detail & Related papers  (2023-07-05T06:03:40Z)
• A Survey of Large Language Models [81.06947636926638]
  Language modeling has been widely studied for language understanding and generation in the past two decades. Recently, pre-trained language models (PLMs) have been proposed by pre-training Transformer models over large-scale corpora. To discriminate the difference in parameter scale, the research community has coined the term large language models (LLM) for the PLMs of significant size.
  arXiv  Detail & Related papers  (2023-03-31T17:28:46Z)
• Technology Readiness Levels for Machine Learning Systems [107.56979560568232]
  Development and deployment of machine learning systems can be executed easily with modern tools, but the process is typically rushed and means-to-an-end. We have developed a proven systems engineering approach for machine learning development and deployment. Our "Machine Learning Technology Readiness Levels" framework defines a principled process to ensure robust, reliable, and responsible systems.
  arXiv  Detail & Related papers  (2021-01-11T15:54:48Z)

This list is automatically generated from the titles and abstracts of the papers in this site.

This site does not guarantee the quality of this site (including all information) and is not responsible for any consequences.