Enterprise AI Analysis
Venus: An Efficient Edge Memory-and-Retrieval System for VLM-based Online Video Understanding
This paper introduces Venus, an on-device memory-and-retrieval system for efficient online video understanding. Venus builds upon our previously proposed edge-cloud disaggregated architecture to further address the technical challenges of practical deployment.
Executive Impact
Venus achieves a 15x-131x speedup in total response latency compared to state-of-the-art methods, while maintaining comparable or even superior reasoning accuracy for VLM-based online video understanding.
0x
Latency Speedup
0x
Max Speedup
0%
Reasoning Accuracy
Deep Analysis & Enterprise Applications
Select a topic to dive deeper, then explore the specific findings from the research, rebuilt as interactive, enterprise-focused modules.
Performance
Architecture
Comparison
Applications
Unprecedented Performance Gains
Our analysis reveals significant performance improvements in VLM-based online video understanding, primarily driven by Venus's efficient edge-cloud disaggregated architecture and adaptive keyframe retrieval. By minimizing redundant data and optimizing inference, Venus dramatically reduces latency while maintaining high accuracy.
15x - 131x
Latency Speedup Compared to SOTA Baselines
Edge-Cloud Disaggregated Workflow
Venus employs a novel edge-cloud disaggregated architecture that performs multimodal memory construction and keyframe retrieval on the edge, with VLM reasoning in the cloud. The architecture comprises two main stages: Ingestion and Querying.
Enterprise Process Flow
Streaming Video Frames
→
Scene Segmentation & Clustering
→
Memory Construction (MEMs + AuxModels)
→
Hierarchical Memory Management
→
User Query & Similarity Calculation
→
Adaptive Keyframe Sampling
→
Cloud-hosted VLM Reasoning
Venus vs. Existing VLM Systems
A detailed comparison highlights the unique advantages of Venus in deployment efficiency, real-time processing, and intelligent memory management, distinguishing it from conventional approaches.
| Feature | Existing Methods | Venus System |
|---|---|---|
| Deployment Model | Cloud-only (high bandwidth/latency) or Edge-Cloud (heavy edge processing) | ✓ Edge-Cloud (light edge, cloud reasoning) |
| Real-time Processing | Limited by latency & compute capacity | ✓ Enabled by scene segmentation & clustering for sparse indexing |
| Memory Management | Redundant frames, inefficient storage, poor retrieval | ✓ Hierarchical, sparse index, efficient recall & retrieval |
| Keyframe Selection | Greedy Top-K (lack diversity, redundancy) | ✓ Adaptive Sampling (relevance & diversity, cost-adaptive) |
| Total Latency | High (communication, cloud/edge compute) | ✓ 15x-131x faster with real-time responses |
| Reasoning Accuracy | Variable (prone to information loss) | ✓ Comparable or superior, robust to diverse queries |
Real-world Application: Smart Home
Explore a practical scenario where Venus excels in providing intelligent online video understanding, demonstrating its value in multimodal personal assistants, smart surveillance, and city scene reasoning.
Smart Home Scenario
In a smart-home setting, Venus allows family members to query current or historical video segments, such as recalling a cooking process or verifying if an elderly person took their medication. The system efficiently processes streaming video, builds a contextual memory on the edge, and leverages cloud VLMs for real-time, accurate reasoning. This enables proactive monitoring and immediate responses without the typical latency overhead of full cloud processing or the computational burden of heavy edge inference.
Calculate Your Potential Savings
Estimate the efficiency gains and cost savings by deploying an Edge AI system like Venus in your enterprise.
Estimated Annual Savings
Annual Hours Reclaimed
Your Implementation Roadmap
A structured approach to integrating Venus into your existing infrastructure for maximum impact.
Phase 1: Edge Integration & Memory Setup
Deploy Venus on edge devices, configure streaming ingestion, and establish the hierarchical memory with initial indexing.
Phase 2: VLM Integration & Querying API
Integrate with cloud-hosted VLM services via API, set up query encoding, and test initial keyframe retrieval.
Phase 3: Adaptive Sampling & Optimization
Fine-tune adaptive keyframe sampling, conduct performance benchmarks, and optimize for specific application scenarios.
Phase 4: Scalable Deployment & Monitoring
Roll out across target infrastructure, implement monitoring for performance and accuracy, and establish continuous improvement loops.
Ready to Transform Your Video Understanding?
Book a personalized session with our AI strategists to explore how Venus can revolutionize your enterprise operations.
Ready to Get Started?
Book Your Free Consultation.
Let's Discuss Your AI Strategy!
Lets Discuss Your Needs
Select a Date
Sun
Mon
Tue
Wed
Thu
Fri
Sat