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cloud.google.com·7h ago
AI Research
arXiv CS.AI·12d ago·5 min read
AS$^2$D: Accelerating On-Demand Audio Understanding on Mobile Devices
TL;DR
AS$^2$D introduces a novel approach to audio understanding on mobile devices by decoupling the drafting and verification processes, resulting in a 42-76% increase in throughput for automatic speech recognition (ASR). This method allows for more efficient inference by leveraging audio and user input without being tied to the target's evolving output.
✦ Why It Matters
Consider implementing AS$^2$D in your mobile audio applications to significantly boost performance.
Key Takeaways
- Explore AS$^2$D for improved audio processing in mobile apps.
- Utilize target-decoupled drafting to enhance throughput.
- Test AS$^2$D across different devices for optimal performance.
- Leverage audio input for more efficient candidate generation.
How It Works
AS$^2$D separates the drafting and verification processes in audio understanding. The drafter generates candidates based on its own history and the audio input, while the target model independently verifies these candidates.
This decoupling allows both processes to run concurrently, significantly increasing throughput and efficiency.