Conditioned RNN Output Generation for Real-Time N-Bit Sampling
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Solution Overview
Problem
Existing neural network systems face challenges in generating accurate output examples efficiently, particularly in environments with limited computational resources such as mobile devices.
Innovation Solution
The system generates output examples using a recurrent neural network by splitting the output value into two halves, generating the values of the first half and then the second half conditioned on the first half, reducing the number of sequential matrix-vector product computations and optimizing for real-time implementation.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Productivity
If the system generates N-bit output values using a recurrent neural network in real-time on resource-constrained devices, then the computational and memory requirements become prohibitively high, but reducing the computational approach compromises output generation accuracy
Solution Approach 1:
The patent divides the N-bit output value generation into two separate stages: first generating the most significant N/2 bits, then generating the least significant N/2 bits. This segmentation reduces the output space size from 2^N to 2^(N/2), thereby reducing the number of sequential matrix-vector product computations required at each time step while maintaining the ability to generate accurate N-bit output values through conditioned generation
2Productivity
If the system reduces the number of sequential matrix-vector product computations by splitting output generation into two halves, then computational efficiency improves, but the complexity of the generation process increases
Solution Approach 1:
The patent performs preliminary generation of the most significant N/2 bits first, using these generated values as conditioning input for the subsequent generation of the least significant N/2 bits. This preliminary action structure allows the system to break down the complex N-bit generation task into two manageable stages, where each stage operates with reduced computational complexity while the conditioning mechanism ensures overall generation accuracy
Data Source
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AI summary
Methods, systems, and apparatus, including computer programs encoded on computer storage media, for generating output examples using neural networks. One of the methods includes, at each generation time step, processing a first recurrent input comprising an N-bit output value at the preceding generation time step in the sequence using a recurrent neural network and in accordance with a hidden state to generate a first score distribution; selecting, using the first score distribution, values for the first half of the N bits; processing a second recurrent input comprising (i) the N-bit output value at the preceding generation time step and (ii) the values for the first half of the N bits using the recurrent neural network and in accordance with the same hidden state to generate a second score distribution; and selecting, using the second score distribution, values for the second half of the N bits of the output value.