Cascade: A Platform for Delay-Sensitive Edge Intelligence
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Abstract (arXiv)
Interactive intelligent computing applications are increasingly prevalent, creating a need for AI/ML platforms optimized to reduce per-event latency while maintaining high throughput and efficient resource management. Yet many intelligent applications run on AI/ML platforms that optimize for high throughput even at the cost of high tail-latency. Cascade is a new AI/ML hosting platform intended to untangle this puzzle. Innovations include a legacy-friendly storage layer that moves data with minimal copying and a "fast path" that collocates data and computation to maximize responsiveness. Our evaluation shows that Cascade reduces latency by orders of magnitude with no loss of throughput.
Key facts. Cited by 1 paper in the map (data through 14 Oct 2026). Builds on 5 of its 63 references that are in the map. First posted on arXiv 29 Nov 2023.
Builds on
5 of 63 references.
References · 5
- 2022StrongSORT: Make DeepSORT Great Againcited by 213
- 2020From Goals, Waypoints & Paths To Long Term Human Trajectory Forecastingcited by 167
- 2019Learning Generalisable Omni-Scale Representations for Person Re-Identificationcited by 74
- 2019Real-time Anomaly Detection and Classification in Streaming PMU Datacited by 4
- 2021Spindle: Techniques for Optimizing Atomic Multicast on RDMAcited by 1
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Citing papers per year: 2023: 1.
1 paper.