Feedforward Generative Neural Networks for Low-Latency Output
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Solution Overview
Problem
Autoregressive neural networks require significant computational resources and time to generate output examples due to their sequential processing nature, limiting their use in environments with limited resources or requiring low latency.
Innovation Solution
A feedforward generative neural network architecture that generates output examples in a single inference step, trained to match the quality of autoregressive networks using techniques like divergence, magnitude, perceptual, and contrastive losses, reducing computational complexity and latency.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Manufacturing precision
If autoregressive neural networks are used to generate output examples, then the quality of generated outputs is improved, but the computational resources and time required increase significantly
Solution Approach 1:
The patent segments the generation process into two distinct phases: (1) training phase where an autoregressive model learns from high-quality data, and (2) inference phase where a simplified feedforward model generates outputs. This segmentation allows the complex autoregressive computations to be confined to training only, while inference uses a lighter model.
Solution Approach 2:
The patent performs preliminary action by training the autoregressive model in advance during the training phase. The learned parameters and patterns from this preliminary training are then transferred to the feedforward model, enabling it to generate high-quality outputs without requiring the computational overhead of autoregressive processing during inference.
2Manufacturing precision
If autoregressive neural networks are used to generate output examples, then the quality of generated outputs is improved, but the time required for generation increases
Solution Approach 1:
The patent divides the overall process into training time (where quality is optimized using autoregressive methods) and inference time (where speed is prioritized using feedforward methods). This temporal segmentation resolves the contradiction by allowing quality optimization during training without sacrificing inference speed.
Solution Approach 2:
The autoregressive model performs preliminary learning during training to capture complex patterns and relationships. Once trained, the feedforward model can generate outputs quickly by applying these pre-learned patterns, eliminating the need for sequential autoregressive processing during time-critical inference operations.
3Device complexity
If feedforward generative neural network is used, then computational requirements and latency are reduced, but the quality of output generation must be maintained
Solution Approach 1:
The patent creates a simplified copy of the autoregressive model's capabilities in the form of a feedforward model. During training, the feedforward model learns to replicate the quality characteristics of the autoregressive model by minimizing the difference between their outputs. This copying approach allows the feedforward model to achieve comparable quality with reduced computational requirements.
Solution Approach 2:
The patent changes the parameters of the feedforward model during training by adjusting weights and biases to match the distribution and quality characteristics of the autoregressive model's outputs. This parameter optimization ensures that the simplified feedforward architecture can produce outputs of comparable quality despite having fewer computational resources.
Data Source
AI summary
A feedforward generative neural network that generates an output example that includes multiple output samples of a particular type in a single neural network inference. Optionally, the generation may be conditioned on a context input. For example, the feedforward generative neural network may generate a speech waveform that is a verbalization of an input text segment conditioned on linguistic features of the text segment.