State Space Neural Network Codecs for Low-Power Data Compression
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Solution Overview
Problem
Existing data compression techniques face challenges in adaptability and efficiency, particularly for edge devices, due to computational and power constraints, and are often domain-specific, leading to suboptimal performance and high resource usage.
Innovation Solution
Integration of state space neural networks (SSNNs) into data-driven compression codecs, leveraging mathematical methods from state space modeling to efficiently represent time series data, reducing computational overhead and improving compression ratios.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If traditional deep learning models are used for data compression, then pattern recognition capability is improved, but computational complexity and power consumption increase
Solution Approach 1:
The patent transforms the neural network architecture from traditional deep learning parameters to state space model parameters (state transition matrices, input matrices). This parameter transformation enables the model to maintain pattern recognition capabilities while reducing computational complexity to linear time complexity, making it suitable for edge devices with power constraints
Solution Approach 2:
The patent replaces the traditional deep learning mechanical system (multiple layers of neurons with activation functions) with a state space model system (linear state transitions with nonlinear output). This substitution maintains the essential function of pattern recognition while dramatically reducing the computational burden and power consumption
2Manufacturing precision
If hand-designed compression codecs are used, then domain-specific performance is improved, but adaptability to different data types deteriorates
Solution Approach 1:
The patent creates a universal compression framework based on state space models that can handle multiple data types (time series, images, audio) through a single unified architecture. The model learns domain-specific patterns automatically during training without requiring hand-designed domain-specific algorithms, thus achieving both universality and domain-specific performance
3Measurement precision
If recurrent neural networks are used for compression, then temporal pattern recognition is improved, but computational efficiency on specialized hardware deteriorates
Solution Approach 1:
Instead of using traditional recurrent neural networks that process sequences through hidden states, the patent inverts the approach by using state space models where the state transitions are explicitly defined through linear transformations. This inversion allows the model to maintain temporal pattern recognition while being computationally efficient on hardware designed for recurrent operations, as the state space formulation naturally maps to efficient hardware implementations
Data Source
AI summary
A method and system for compressing and decompressing digital data employs a codec comprising state space neural network (SSNN) layers. The encoder comprises one or more SSNN layers; when a plurality of SSNN layers are used, they may be arranged with decreasing dimensionality. The decoder also comprises one or more SSNN layers, and when a plurality of SSNN layers are used, they may be arranged with increasing dimensionality. The method and system may also include quantization of compressed data, and additional pre- and post-processing of input and output data. The quantizer may also comprise SSNN layers. The codec and quantizer may be optimized together or separately, for example using a loss metric.