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cloud.google.com·7h ago
AI Research
arXiv CS.AI·12d ago·5 min read
OmniTide: Co-Designing Algorithms and Systems for Efficient On-Device Omni-LLM Streaming
TL;DR
OmniTide is a novel algorithm-system co-design that enhances on-device streaming for omni-modal inference, achieving up to 12.72x speedups and 2.40x lower latency. It effectively manages memory and improves accuracy by up to 18 percentage points over traditional methods.
✦ Why It Matters
Implement OmniTide to enhance your on-device AI applications with improved speed and accuracy today.
Key Takeaways
- Adopt OmniTide for efficient omni-modal inference on edge devices.
- Utilize OmniPick to prioritize critical multimodal context.
- Implement OmniPage to minimize memory fragmentation.
- Evaluate performance improvements using StreamingBench benchmarks.
Full Summary
OmniTide addresses the challenges of on-device streaming for omni-modal inference, which combines multiple data types while ensuring user privacy and reducing costs. Traditional methods struggle with memory limitations and latency due to the growth of key-value (KV) caches.
OmniTide introduces a dual-component approach: OmniPick, which retains essential multimodal context based on importance, and OmniPage, which optimizes memory usage by partitioning and compacting data. Evaluations on various benchmarks demonstrate that OmniTide can achieve kernel speedups of up to 12.72 times and reduce stream-loop latency by 2.40 times.
Additionally, it enhances accuracy by up to 18 percentage points compared to sliding-window methods while decreasing physical KV span by 26.7%. This innovation enables real-time, infinite-context streaming on edge devices, making it a significant advancement in efficient AI processing.