{"title":"A Quantitative Analysis of State Space Model-Based Large Language Model: Study of Hungry Hungry Hippos","authors":"Dongho Yoon;Taehun Kim;Jae W. Lee;Minsoo Rhu","doi":"10.1109/LCA.2024.3422492","DOIUrl":null,"url":null,"abstract":"As the need for processing long contexts in large language models (LLMs) increases, attention-based LLMs face significant challenges due to their high computation and memory requirements. To overcome this challenge, there have been several recent works that seek to alleviate attention's system-level bottlenecks. An approach that has been receiving a lot of attraction lately is state space models (SSMs) thanks to their ability to substantially reduce computational complexity and memory footprint. Despite the excitement around SSMs, there is a lack of an in-depth characterization and analysis on this important model architecture. In this paper, we delve into a representative SSM named Hungry Hungry Hippos (H3), examining its advantages as well as its current limitations. We also discuss future research directions on improving the efficiency of SSMs via hardware architectural support.","PeriodicalId":51248,"journal":{"name":"IEEE Computer Architecture Letters","volume":"23 2","pages":"154-157"},"PeriodicalIF":1.4000,"publicationDate":"2024-07-03","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":"0","resultStr":null,"platform":"Semanticscholar","paperid":null,"PeriodicalName":"IEEE Computer Architecture Letters","FirstCategoryId":"94","ListUrlMain":"https://ieeexplore.ieee.org/document/10584280/","RegionNum":3,"RegionCategory":"计算机科学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":null,"EPubDate":"","PubModel":"","JCR":"Q4","JCRName":"COMPUTER SCIENCE, HARDWARE & ARCHITECTURE","Score":null,"Total":0}
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Abstract
As the need for processing long contexts in large language models (LLMs) increases, attention-based LLMs face significant challenges due to their high computation and memory requirements. To overcome this challenge, there have been several recent works that seek to alleviate attention's system-level bottlenecks. An approach that has been receiving a lot of attraction lately is state space models (SSMs) thanks to their ability to substantially reduce computational complexity and memory footprint. Despite the excitement around SSMs, there is a lack of an in-depth characterization and analysis on this important model architecture. In this paper, we delve into a representative SSM named Hungry Hungry Hippos (H3), examining its advantages as well as its current limitations. We also discuss future research directions on improving the efficiency of SSMs via hardware architectural support.
期刊介绍：
IEEE Computer Architecture Letters is a rigorously peer-reviewed forum for publishing early, high-impact results in the areas of uni- and multiprocessor computer systems, computer architecture, microarchitecture, workload characterization, performance evaluation and simulation techniques, and power-aware computing. Submissions are welcomed on any topic in computer architecture, especially but not limited to: microprocessor and multiprocessor systems, microarchitecture and ILP processors, workload characterization, performance evaluation and simulation techniques, compiler-hardware and operating system-hardware interactions, interconnect architectures, memory and cache systems, power and thermal issues at the architecture level, I/O architectures and techniques, independent validation of previously published results, analysis of unsuccessful techniques, domain-specific processor architectures (e.g., embedded, graphics, network, etc.), real-time and high-availability architectures, reconfigurable systems.
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