# How a Memristor Circuit Adjusts AI Network Weights

> This patent describes a specialized electronic circuit that uses memristors to store and adjust the 'weights' of an artificial neural network, making AI calculations more efficient.

- **Patent:** US 12056602
- **Original title:** Circuit for calculating weight adjustments of an artificial neural network, and a module implementing a long short-term artificial neural network
- **Owner:** Qatar Foundation
- **Granted:** 2024
- **Status:** Active
- **Times cited:** 1
- **Field:** semiconductors, ai_ml, consumer_electronics, telecommunications

## What it does

This circuit implements a multilayer artificial neural network using a 'synaptic grid array' of memristors (Claim 1). These memristors store the network's 'weights,' which are crucial for AI learning. A 'calculation controller' (Claim 1) adjusts these weights. For example, during a 'read process,' the circuit uses PMOS and NMOS transistors to apply positive and negative voltage signals to a memristor. This temporarily changes its resistance to calculate the network's output, then returns it to its original state (Claim 3). The controller also includes modules for 'local gradient computation,' 'momentum computation,' and 'adaptive learning rate' to refine how weights are adjusted during the learning process (Claim 6).

## What it does NOT cover

- Does not cover artificial neural networks implemented purely in software without specialized memristor hardware.
- Does not cover neural networks that store their weights using traditional silicon-based memory like SRAM or DRAM instead of memristors.
- Does not cover memristor-based circuits that use different transistor types or control mechanisms for input signals than the specified PMOS and NMOS transistors.
- Does not cover weight adjustment methods that do not include local gradient, momentum, or adaptive learning rate computations as part of the controller.
- Does not cover read processes where the memristor's resistance is not returned to its original state after the reading operation.

## The clever bit

The novelty lies in the precise circuit design that combines memristors with MOS transistors for controlled, reversible weight reading and adjustment. It integrates key AI training algorithm components like gradient, momentum, and adaptive learning rate directly into the hardware, which can speed up and optimize the learning process on the chip itself.

## Real-world examples

1. AI accelerator chips
2. Neuromorphic processors
3. Edge AI devices
4. Specialized hardware for deep learning inference

## Why it matters

Artificial neural networks are at the heart of modern AI, but they require significant computing power and energy. This patent addresses these challenges by proposing a hardware-based solution using memristors. Memristors offer the potential for more energy-efficient and faster AI computations by directly integrating memory and processing, which is vital for advanced AI applications and edge devices.

## Frequently asked questions

### What does How a Memristor Circuit Adjusts AI Network Weights cover?

This patent describes a specialized electronic circuit that uses memristors to store and adjust the 'weights' of an artificial neural network, making AI calculations more efficient.

### Who owns patent US 12056602?

Qatar Foundation owns this patent, granted in 2024.

### When does this patent expire?

This patent is expected to expire on September 26, 2040, when the invention enters the public domain.

### What is patent US 12056602 cited by?

This patent has been cited by 1 later patents that build on its ideas.

### What problem does this patent solve?

Artificial neural networks are at the heart of modern AI, but they require significant computing power and energy. This patent addresses these challenges by proposing a hardware-based solution using memristors. Memristors offer the potential for more energy-efficient and faster AI computations by directly integrating memory and processing, which is vital for advanced AI applications and edge devices.

### What does this patent NOT cover?

Does not cover artificial neural networks implemented purely in software without specialized memristor hardware.

**Full plain-English explainer:** https://patentbrief.org/patent/us/12056602/circuit-for-calculating-weight-adjustments-of-an-artificial-neural-network-and-a

**Original patent:** https://patents.google.com/patent/US12056602

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_Source: PatentBrief — https://patentbrief.org. Patent facts are from public records; the plain-English explanation is PatentBrief's._