How to Save and Reuse Skills Learned by Artificial Intelligence Hardware

A method for capturing the internal settings of a neuromorphic AI chip after it learns a task, allowing that 'skill' to be exported and loaded onto another AI chip.

What it covers

This patent describes a way to extract the specific configuration of an AI hardware device—specifically a neuromorphic chip—after it has learned a task. Instead of just saving software code, the system captures the 'control values' stored in the chip's synaptic registers, which include parameters like neurotransmitter levels, dendrite delays, and axonal delays. These values effectively act as a snapshot of the device's learned behavior. This snapshot can then be stored in a library and transferred to a second, similar AI device, allowing it to perform the same task without needing to undergo the original training process.

What it doesn't cover

• —Does not cover software-based neural networks running on standard CPUs or GPUs.
• —Does not cover the specific algorithms used to train the initial neural network.
• —Does not cover the transfer of raw data or training datasets, only the resulting synaptic control values.
• —Does not cover cloud-based model weight sharing (e.g., standard TensorFlow or PyTorch model exports).

The clever bit

It treats the physical state of a silicon-based neural network (like synaptic delays and neurotransmitter levels) as a portable data object, effectively creating a 'file format' for hardware-level intelligence.

Why it matters

This patent addresses a major bottleneck in neuromorphic computing: how to move learned 'intelligence' between physical hardware chips. By treating learned neural states as portable files, it enables a modular approach to AI hardware, where specialized skills can be developed on one device and deployed across a fleet of edge devices without retraining.

Real-world examples

• 1.BrainChip Akida neuromorphic processor
• 2.Edge AI devices that require pre-trained behaviors
• 3.Hardware-based pattern recognition systems