V-Rex: Real-Time Streaming Video LLM Acceleration via Dynamic KV Cache Retrieval
Revolutionizing Real-Time Streaming Video LLM Inference
V-Rex addresses the memory and computational challenges of streaming video LLMs by introducing ReSV, a training-free dynamic KV cache retrieval algorithm, and a co-designed hardware accelerator (DRE). It achieves real-time inference on edge devices with significant speedup and energy efficiency by optimizing KV cache retrieval during the iterative prefill stage, leveraging temporal and spatial similarity, and dynamically adjusting token selection.
Unprecedented Performance & Efficiency
V-Rex delivers breakthrough real-time capabilities for streaming video LLMs on resource-constrained edge devices.
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Speedup over GPU
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Energy Efficiency
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FPS on Edge (Max)
Deep Analysis & Enterprise Applications
Select a topic to dive deeper, then explore the specific findings from the research, rebuilt as interactive, enterprise-focused modules.
ReSV (Retrieval for Streaming Video) is a training-free dynamic KV cache retrieval algorithm that leverages spatial-temporal token clustering and weighted cumulative sum (WiCSum) thresholding to reduce KV cache memory across video frames. It dynamically adjusts token selection per transformer layer and attention head, minimizing fetched tokens without significant accuracy loss.
The Dynamic KV Cache Retrieval Engine (DRE) is a compact, low-latency hardware accelerator co-designed with ReSV. It features bit-level and early-exit based computing units, along with hierarchical KV cache memory management, to efficiently handle the irregular and data-dependent operations of ReSV, offloading these tasks from the main LLM engine for peak efficiency.
V-Rex integrates ReSV and DRE into a unified software-hardware co-design, enabling real-time streaming video LLM inference on resource-constrained edge devices. This integration ensures efficient management of KV cache, reduced data transfer, and optimized computation, delivering significant speedup and energy efficiency gains.
Key Challenge Addressed
79% KV Retrieval Latency Overhead in Prefill StageEnterprise Process Flow
SW-Level Optimizations (ReSV)
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HW-Level Optimizations (DRE)
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Real-time Streaming Video LLM Inference
| Feature | Existing Methods (e.g., InfiniGen) | V-Rex |
|---|---|---|
| Target Stage | Generation Stage | Iterative Prefill & Generation |
| KV Cache Management | Fixed Top-K Selection | Dynamic WiCSum Thresholding & Hash-bit Clustering |
| Memory Overhead | High (Full Cache Offload) | Low (Selective Fetch, Hierarchical Memory) |
| Edge Device Efficiency | Poor due to PCIe bottleneck | Excellent (SW-HW Co-design, DRE) |
| Accuracy | Degrades with aggressive pruning | Maintains accuracy with dynamic selection |
Impact on Edge Deployment
For resource-constrained edge devices like AGX Orin, traditional methods quickly hit memory limits (OOM). V-Rex, with its compact DRE and efficient ReSV algorithm, extends operational time reliably beyond 20K tokens, sustaining 7 FPS even at large sequence lengths. This enables real-time applications where prior solutions fail.
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Reliable Operation (Tokens)
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Sustained FPS (large sequences)
Calculate Your Potential ROI
See how V-Rex can translate into tangible savings and reclaimed productivity for your enterprise.
Projected Annual Savings
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Annual Hours Reclaimed
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Your Strategic Implementation Roadmap
A phased approach to integrate V-Rex seamlessly into your enterprise ecosystem.
Phase 1: Initial Assessment & Pilot (2-4 Weeks)
Evaluate existing infrastructure, identify key use cases for V-Rex, and deploy a pilot on a selected edge device to validate performance and accuracy with real-world streaming data.
Phase 2: Full-Scale Integration & Customization (4-8 Weeks)
Integrate V-Rex into broader enterprise workflows, customize token selection policies and hardware configurations to specific application needs, and conduct extensive testing for reliability and scalability.
Phase 3: Optimization & Continuous Improvement (Ongoing)
Monitor system performance, gather user feedback, and apply continuous optimizations to ReSV and DRE through software updates and potential hardware revisions, ensuring maximum efficiency and adaptability to evolving LLM architectures.
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