SOFTWARE-BASED MASS CUSTOMIZATION OF ARTIFICIAL NEURAL NETWORKS

• Main Authors: Milbrath, Jordan, Rivard, Jonathan M, Straub, Jeremy, Rosch-Grace, Dominic
• Format: Patent
• Language: English
• Published: 19.06.2025
• Subjects: CALCULATING; COMPUTER SYSTEMS BASED ON SPECIFIC COMPUTATIONAL MODELS; COMPUTING; COUNTING; PHYSICS

The subject matter as disclosed herein provides a system and method for improving neural network development efficiency through a visual development interface and customization framework. The system implements a coordinate-based mapping system that enables precise component tracking through spatial identification based on layer position and placement order, which reduces computational overhead. The interface provides drag-and-drop model creation capabilities alongside granular neuron-level customization. Users can modify activation functions, weight initializations, and connectivity patterns for individual neurons. The system supports creation of heterogeneous neural networks with non-uniform architectures and enables implementation of constant node networks and amalgamated configurations. Real-time visualization capabilities provide both high-level architectural views and detailed component-level information. Automated logic generation translates visual representations into optimized backend code. The system delivers measurable technical benefits including improved processing efficiency, reduced resource requirements, and accelerated development processes through automated batch operations and immediate optimization capabilities.