Neural Network Frame Skipping for Speech Recognition
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Solution Overview
Problem
Current neural networks in real-time applications require significant computational effort, leading to conflicts with other GPU-intensive tasks and increased power usage, which limits battery life in mobile devices and reduces classification accuracy when optimizations like SIMD and batched lazy evaluation are employed.
Innovation Solution
Implementing frame skipping techniques and distances on demand, where neural network outputs are calculated only for every Nth time instance, and approximated for skipped frames using extrapolation methods, reducing computational complexity while maintaining accuracy.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Power
If neural network computations are offloaded to GPU, then computational power and classification accuracy are improved, but power consumption increases and conflicts with other GPU-intensive applications
Solution Approach 1:
The patent applies partial action by computing neural network outputs only for every Nth frame (e.g., every 5th frame) rather than every frame. This reduces GPU utilization and power consumption while maintaining sufficient accuracy for speech recognition through selective computation at key intervals.
Solution Approach 2:
The system implements periodic computation where the neural network is evaluated at regular intervals (every Nth frame) rather than continuously. This periodic evaluation reduces the frequency of GPU activation, lowering power consumption and avoiding conflicts with other GPU-intensive applications while still providing timely speech recognition updates.
2Measurement precision
If neural network computations are performed for every frame, then speech recognition accuracy is improved, but computational complexity and processing time increase
Solution Approach 1:
The patent computes neural network outputs partially, only for every Nth frame instead of every frame. This reduces computational complexity by a factor of N while maintaining acceptable speech recognition accuracy by capturing sufficient temporal information at the reduced computation rate.
Solution Approach 2:
The system uses interpolation to estimate intermediate frame outputs by copying and extrapolating from the computed outputs at Nth frames. This creates approximate copies of the neural network outputs for intermediate frames without performing full computations, reducing complexity while preserving accuracy.
3Productivity
If neural network outputs are computed for every frame, then real-time processing capability is improved, but processing speed decreases due to computational load
Solution Approach 1:
The system uses periodic computation at Nth frames to reduce processing load, which increases processing speed. The periodic nature ensures that computations are performed at manageable intervals while still maintaining real-time processing capability through timely updates at the reduced frequency.
Solution Approach 2:
By interpolating and copying outputs from Nth frame computations to intermediate frames, the system avoids redundant full computations. This copying approach maintains real-time processing capability by providing timely estimates without the computational burden of full evaluations at every frame.
Data Source
AI summary
Techniques related to implementing neural networks for speech recognition systems are discussed. Such techniques may include implementing frame skipping with approximated skip frames and/or distances on demand such that only those outputs needed by a speech decoder are provided via the neural network or approximation techniques.