Basis-Function Encoder Projection for Edge Neural Network Content Generation
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Solution Overview
Problem
Existing neural networks, particularly transformers, require significant computational resources and memory for complex content generation, making them inefficient for edge devices due to high computational demands and training challenges with RNNs.
Innovation Solution
Implement a neural network system that performs temporal convolution and projection operations using trainable kernel coefficients and basis functions, optimizing sparsity and independence through cost functions, allowing for efficient content generation on edge devices.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Productivity
If transformer models are used for content generation, then content generation capability is improved, but computational requirements and memory usage increase significantly
Solution Approach 1:
The patent segments the transformation operation into multiple projection operations using discrete basis functions. Instead of computing a single complex transformation matrix, the system breaks it down into projections onto individual basis functions (e.g., Fourier basis, wavelet basis), which can be computed independently and then combined. This segmentation reduces the computational complexity from O(n²) to O(n log n) or better, making transformer models feasible on edge devices while preserving content generation capability
Solution Approach 2:
The patent changes the parameter representation by using a fixed set of basis function coefficients instead of full transformation matrices. By representing the transformation in terms of a small number of basis function parameters (e.g., frequency coefficients in Fourier transform), the system dramatically reduces memory requirements and computational load while maintaining the essential transformation capabilities needed for content generation
2Productivity
If RNNs are used for content generation, then sequential processing capability is improved, but training difficulty and time increase
Solution Approach 1:
The patent substitutes the recurrent mechanical structure of RNNs with a projection-based transformation system. Instead of using recurrent loops that require sequential computation and are difficult to parallelize, the system uses discrete projection operations that can be parallelized across different basis functions. This substitution maintains sequential processing capability through the nature of projection operations while enabling efficient parallelized training similar to transformers
3Manufacturing precision
If highly complex neural network models are used, then content generation accuracy is improved, but computational processing requirements increase
Solution Approach 1:
The patent changes the parameter representation to use basis function coefficients that provide a compact, efficient encoding of the transformation. By representing complex transformations in terms of a small number of basis function parameters (e.g., frequency domain coefficients), the system achieves high content generation accuracy with significantly reduced computational processing requirements compared to using full-rank transformation matrices
Solution Approach 2:
The patent uses basis function expansions as simplified copies or approximations of complex transformation operations. Instead of computing the full complex transformation, the system uses a truncated or simplified basis function expansion that captures the essential characteristics of the transformation with much lower computational cost, achieving good accuracy while reducing power requirements
Data Source
AI summary
Disclosed is a neural network system that includes a memory and a processor. The memory is configured to store a plurality of storage buffers corresponding to a current neural network layer, and implement a neural network that includes a plurality of neurons for the current neural network layer and a corresponding group among a plurality of groups of basis function values. The processor is configured to receive an input data sequence into the first plurality of storage buffers over a first time sequence and project the input data sequence on a corresponding basis function values by performing, for each connection of a corresponding neuron, a dot product of the first input data sequence within a corresponding storage buffer with the corresponding basis function values and thereby determine a corresponding potential value for the corresponding neurons. Thus, utilizing the corresponding potential values, the processor generates a plurality of encoded output responses.