Tag-Based Neural Network Description Framework
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Solution Overview
Problem
Existing neuronal models and systems lack a flexible and user-friendly method for designing neural networks, requiring substantial specialized knowledge and platform-specific customization to achieve efficient performance, especially in real-time applications like autonomous robotics.
Innovation Solution
The High Level Network Description (HLND) framework allows for a unified, human-readable, and machine-interpretable representation of neuromorphic network architectures, enabling users to define nodes, connections, and dynamic partitioning using tags and Boolean operations, facilitating the creation of complex neural networks without extensive expertise.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Ease of operation
If existing neuronal models and systems are used, then neural networks can be implemented, but they require substantial specialized knowledge and platform-specific customization to achieve efficient performance
Solution Approach 1:
The patent introduces an intermediary layer (the HLND framework with tag-based syntax) between the user and the underlying neuronal models. This intermediary provides a simplified, unified interface that translates high-level design specifications into platform-specific implementations, eliminating the need for users to directly engage with complex specialized knowledge while maintaining efficient performance across different hardware platforms.
Solution Approach 2:
The patent creates a universal framework that can operate across multiple different hardware platforms and neuronal model implementations. The tag-based HLND syntax serves as a platform-independent language that can describe neural network architectures universally, allowing the same high-level description to be compiled or interpreted for different target systems without requiring platform-specific customization knowledge.
2Adaptability or versatility
If existing neuronal models are used, then neural network functionality is achieved, but flexibility in designing network architectures is limited
Solution Approach 1:
The patent segments the neural network design process into distinct, manageable components using tags. Each tag represents a specific functional element or group of elements (e.g., input layers, hidden layers, output layers, connection types), allowing users to design complex architectures by combining simple, standardized building blocks. This segmentation increases flexibility while reducing design complexity through modularity.
Solution Approach 2:
The patent introduces dynamic capabilities to the network design framework through tags that can represent variable properties such as connection probabilities, learning rates, and activation functions. These tagged parameters can be modified during network creation and execution, allowing flexible adaptation of network architecture without requiring complete redesign, thus enhancing versatility while maintaining simplicity.
Data Source
AI summary
Apparatus and methods for high-level neuromorphic network description (HLND) using tags. The framework may be used to define nodes types, define node-to-node connection types, instantiate node instances for different node types, and/or generate instances of connection types between these nodes. The HLND format may be used to define nodes types, define node-to-node connection types, instantiate node instances for different node types, dynamically identify and/or select network subsets using tags, and/or generate instances of one or more connections between these nodes using such subsets. To facilitate the HLND operation and disambiguation, individual elements of the network (e.g., nodes, extensions, connections, I/O ports) may be assigned at least one unique tag. The tags may be used to identify and/or refer to respective network elements. The HLND kernel may comprises an interface to Elementary Network Description.