Variational Autoencoder for Adaptive Hyperdimensional Encoding
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Solution Overview
Problem
Existing hyperdimensional computing (HDC) encoders are static and unreliable, failing to adapt to changes in environment and data uncertainty, which affects the quality and reliability of hyperdimensional learning models, especially in dynamic IoT systems.
Innovation Solution
A variational autoencoder (VAE) module is introduced to generate an unsupervised network that dynamically adjusts the HDC representation, enabling adaptive learning and robust single-pass and iterative learning through a formal loss function and training method, allowing the HDC model to update and adapt to new data.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Productivity
If a deterministic HDC encoder is used, then the encoding process is simple and fast, but the flexibility and reliability adapt to changes in environment and data uncertainty
Solution Approach 1:
The patent applies dynamics by transitioning from a static deterministic encoder to a dynamic probabilistic encoder. The VAE module continuously adapts its encoding parameters based on input data distributions, allowing the HDC representation to evolve with changing environments while maintaining computational efficiency through variational inference.
Solution Approach 2:
The patent changes the fundamental parameters of the encoding process from deterministic fixed transformations to probabilistic distributions. The encoder outputs probability distributions over HDC representations rather than fixed vectors, enabling the system to adjust to data uncertainty and environmental changes through parameter learning.
2Use of energy by moving object
If a deterministic HDC encoder is used, then the computational cost is low, but the quality and reliability of hyperdimensional learning models
Solution Approach 1:
The patent implements feedback mechanisms where the learning model's performance information feeds back to refine the VAE encoder's probability distributions. This iterative feedback loop allows the system to improve reliability by adjusting encoding parameters based on actual learning outcomes while controlling computational costs through efficient variational inference.
Solution Approach 2:
The system dynamically adjusts the complexity of the probabilistic encoding based on data characteristics and learning requirements. When data uncertainty is high, the system increases the complexity of the probability distributions to improve reliability. When data is stable, simpler deterministic encoding suffices, optimizing the balance between computational cost and reliability.
3Stability of the object's composition
If traditional HDC encoding is used, then the representation is fixed, but the ability to adapt to changes in data complexity and uncertainty
Solution Approach 1:
The patent changes the encoding representation from fixed stable values to adaptive probability distributions. The VAE module learns to represent data as distributions with varying parameters (mean, variance) that automatically adjust to data complexity levels, allowing the system to maintain stability for simple data while adapting to complex data through parameter learning.
Solution Approach 2:
The patent adds an additional dimension of probability distributions to the traditional fixed HDC representation. Instead of representing data as single points in HDC space, the system represents them as probability distributions, adding a dimensional layer that enables adaptation to data complexity while maintaining computational efficiency.
Data Source
AI summary
A hyperdimensional learning framework is disclosed with a variational encoder (VAE) module that is configured to generate variational autoencoding and to generate an unsupervised network that receives a data input and learns to predict the same data in an output layer. A hyperdimensional computing (HDC) learning module is coupled to the unsupervised network through a data bus, wherein the HDC learning module is configured to receive data from the VAE module and update an HDC model of the HDC learning module. The disclosed hyperdimensional learning framework provides a foundation for a new class of variational autoencoder that ensures that latent space has an ideal representation for hyperdimensional learning. Further disclosed is a hyperdimensional classification that directly operates over encoded data and enables robust single-pass and iterative learning while defining a first formal loss function and training method for HDC.