{
  "patent_number": "US 12056602",
  "country": "US",
  "title": "How a Memristor Circuit Adjusts AI Network Weights",
  "original_title": "Circuit for calculating weight adjustments of an artificial neural network, and a module implementing a long short-term artificial neural network",
  "summary": "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.",
  "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."
  ],
  "filed": "2020-09-26",
  "granted": "2024-08-06",
  "expires": "2040-09-26",
  "status": "active",
  "holder": "Qatar Foundation",
  "holder_url": "https://patentbrief.org/company/qatar-foundation",
  "inventors": [
    {
      "name": "Shiping Wen",
      "url": "https://patentbrief.org/inventor/shiping-wen"
    },
    {
      "name": "Yin Yang",
      "url": "https://patentbrief.org/inventor/yin-yang"
    },
    {
      "name": "Tingwen Huang",
      "url": "https://patentbrief.org/inventor/tingwen-huang"
    }
  ],
  "times_cited": 1,
  "tags": [
    "semiconductors",
    "ai_ml",
    "consumer_electronics",
    "telecommunications"
  ],
  "abstract": "A circuit structure for implementing a multilayer artificial neural network, the circuit comprising: a plurality of memristors implementing a synaptic grid array, the memristors storing weights of the network; and a calculation and control module configured to calculate the value of weight adjustments within the network.",
  "url": "https://patentbrief.org/patent/us/12056602/circuit-for-calculating-weight-adjustments-of-an-artificial-neural-network-and-a",
  "markdown_url": "https://patentbrief.org/patent/us/12056602/circuit-for-calculating-weight-adjustments-of-an-artificial-neural-network-and-a/md",
  "google_patents_url": "https://patents.google.com/patent/US12056602",
  "relatedPatents": []
}