Neural Network Parameter Serialization for Low-Bandwidth Updates
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Solution Overview
Problem
The sheer amount of data required to represent neural networks, particularly in the form of weights and biases, hampers efficient transmission and updating, leading to bottlenecks in data rate and computational complexity.
Innovation Solution
Encoding neural network parameters using context-adaptive arithmetic coding, serialization parameters, and employing numerical representation, layer type, and pointer-based structuring to optimize data stream organization and access.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Quantity of substance
If neural network parameters are stored using standard floating-point formats in container formats, then the representation is simple and widely compatible, but the coded size becomes excessively large, creating a data rate bottleneck
Solution Approach 1:
The patent applies parameter changes by transforming neural network parameters from standard floating-point representation to quantized integer representation. This involves changing the numerical precision and format of the parameters, allowing them to be compressed more efficiently while maintaining acceptable inference accuracy. The quantization process converts high-precision floating-point values into lower-precision integer values that can be encoded with fewer bits.
Solution Approach 2:
The patent extracts only the essential information needed for neural network inference by removing redundant precision. Instead of transmitting or storing the full floating-point values, the system extracts the core parameter values and represents them in a compressed format, discarding less significant digits that have minimal impact on inference quality.
2Quantity of substance
If neural network parameters are compressed using lossy compression, then the coded size is reduced, but the inference quality may deteriorate
Solution Approach 1:
The patent changes the parameter representation from floating-point to quantized integers with carefully selected precision levels. By adjusting the quantization precision and bit-width parameters, the system optimizes the balance between compression ratio and inference accuracy, ensuring that the compressed parameters maintain sufficient fidelity for reliable inference.
Solution Approach 2:
The patent introduces dynamic precision adjustment where different parts of the neural network can be represented with different precision levels. This allows the system to allocate more bits to critical parameters that significantly affect inference quality while using fewer bits for less critical parameters, optimizing the overall trade-off between compression and accuracy.
3Adaptability or versatility
If neural network parameters are transmitted frequently for updates, then the model can be kept current, but the data rate requirements become unsustainable
Solution Approach 1:
The patent transforms parameters into compressed representations that occupy significantly less bandwidth. This parameter transformation enables frequent model updates and fine-tuning transmissions without overwhelming the data rate capacity, as the compressed parameter sets can be transmitted much faster than their uncompressed counterparts.
Solution Approach 2:
The patent performs preliminary compression and optimization of parameter representations before transmission. By pre-quantizing and compressing the parameters on the encoding side, the system reduces the transmission burden, enabling more frequent updates within the available data rate budget.
4Measurement precision
If high-precision floating-point parameters are used, then inference accuracy is maintained, but the computational complexity and memory requirements increase
Solution Approach 1:
The patent changes parameters from floating-point to quantized integer representation, which fundamentally alters the computational operations. Integer arithmetic is generally faster and more energy-efficient than floating-point arithmetic, reducing the computational complexity of inference while maintaining acceptable accuracy through careful quantization design.
Solution Approach 2:
The patent applies partial precision reduction where not all parameters are quantized to the same degree. Critical parameters that most affect accuracy are maintained with higher precision, while less critical parameters use lower precision representations. This selective approach maintains inference accuracy while reducing overall computational complexity.
Data Source
AI summary
Data stream having a representation of a neural network encoded thereinto, the data stream including serialization parameter indicating a coding order at which neural network parameters, which define neuron interconnections of the neural network, are encoded into the data stream.