A Survey on Large Language Model Acceleration based on KV Cache Management (2412.19442v2)

Abstract: LLMs have revolutionized a wide range of domains such as natural language processing, computer vision, and multi-modal tasks due to their ability to comprehend context and perform logical reasoning. However, the computational and memory demands of LLMs, particularly during inference, pose significant challenges when scaling them to real-world, long-context, and real-time applications. Key-Value (KV) cache management has emerged as a critical optimization technique for accelerating LLM inference by reducing redundant computations and improving memory utilization. This survey provides a comprehensive overview of KV cache management strategies for LLM acceleration, categorizing them into token-level, model-level, and system-level optimizations. Token-level strategies include KV cache selection, budget allocation, merging, quantization, and low-rank decomposition, while model-level optimizations focus on architectural innovations and attention mechanisms to enhance KV reuse. System-level approaches address memory management, scheduling, and hardware-aware designs to improve efficiency across diverse computing environments. Additionally, the survey provides an overview of both text and multimodal datasets and benchmarks used to evaluate these strategies. By presenting detailed taxonomies and comparative analyses, this work aims to offer useful insights for researchers and practitioners to support the development of efficient and scalable KV cache management techniques, contributing to the practical deployment of LLMs in real-world applications. The curated paper list for KV cache management is in: \href{https://github.com/TreeAI-Lab/Awesome-KV-Cache-Management}{https://github.com/TreeAI-Lab/Awesome-KV-Cache-Management}.

• The paper presents novel insights into KV cache techniques that reduce memory usage and computation in LLM inference.
• It categorizes optimizations into token-level, model-level, and system-level approaches to guide efficient design.
• Future directions include adaptive cache allocation and hardware-aware designs to enhance scalability and real-world performance.

A Survey on LLM Acceleration Based on KV Cache Management

LLMs have transformed various fields, including natural language processing, computer vision, and multi-modal tasks, owing to their capability to interpret context and execute logical reasoning. Despite these advances, executing LLMs, especially during inference, remains computationally intensive and memory demanding, posing substantial challenges for scalable applications. Key-Value (KV) cache management has emerged as a pivotal approach to mitigate these bottlenecks by decreasing redundant computations and optimizing memory utilization. This paper rigorously surveys KV cache management strategies designed for LLM acceleration, categorizing them into three main optimization strategies: token-level, model-level, and system-level optimizations.

Token-Level Optimization

Token-level optimization techniques focus on fine-tuning the KV cache management process without requiring architectural modifications to the model. These strategies include:

1. KV Cache Selection: Methods such as static and dynamic selection focus on selecting the most relevant tokens for caching to reduce memory footprint without sacrificing performance.
2. KV Cache Budget Allocation: Adaptive memory distribution across tokens ensures efficient cache usage even under constrained memory.
3. KV Cache Merging, Quantization, and Low-rank Decomposition: These methods aim to compress the KV cache, employing strategies like blending similar tokens, reducing bit precision, and decomposing cache matrices into low-rank components to achieve significant compression ratios.

Model-Level Optimization

Model-level optimizations involve altering the model's architecture to inherently support efficient KV cache usage. This survey highlights:

1. Attention Grouping and Sharing: Techniques like Cross-Layer Sharing and Intra-Layer Grouping exploit redundancy in KV cache structures to minimize data duplication.
2. Architecture Alteration: Innovative architectures introduce enhancements such as tailored attention mechanisms and hybrid non-transformer models that maintain performance while optimizing memory and computational requirements.

System-Level Optimization

System-level approaches enhance the efficiency of KV cache management through memory and scheduling optimizations, as well as hardware awareness:

1. Memory Management: Innovations like virtual memory adaptation and prefix sharing improve cache utilization by efficiently managing the allocation and reuse of memory resources.
2. Scheduling: This aspect covers strategies such as prefix-aware and hierarchical scheduling that dynamically manage cache access and update patterns, improving the throughput and latency of inference processes.
3. Hardware-Aware Design: The design incorporates considerations for various computing environments, optimizing for hardware capabilities like GPU, CPU, and other accelerators to efficiently manage KV cache operations.

Implications and Future Directions

The methodologies examined provide significant insights and can potentially extend the application capabilities of LLMs to real-world settings by optimizing inference processes. Future research directions may include developing real-time, task-specific KV cache allocation strategies, incorporating adaptive algorithms that leverage reinforcement learning for intelligent cache management, and exploring distributed coordination strategies in multi-node LLM deployments. Additionally, innovations in hardware design, such as processing-in-memory and computational storage, present new avenues for optimized LLM execution.

This paper presents a comprehensive review and categorization of KV cache management strategies, offering valuable guidance for researchers and practitioners working on developing efficient and scalable LLM inference techniques. Through these approaches, the practical deployment of LLMs can be significantly enhanced, paving the way for advanced applications in AI-driven sectors.