{
  "patent_number": "US 10248907",
  "country": "US",
  "title": "How a Single Electronic Component Can Learn and Process AI Data",
  "original_title": "Resistive processing unit",
  "summary": "This patent describes a tiny electronic component called a resistive processing unit (RPU) that acts like a brain cell in an artificial intelligence network, storing and processing information directly within its changing electrical resistance.",
  "what_it_does": "This patent details a two-terminal resistive processing unit (RPU) that functions as a neuron in a neural network. Its electrical resistance represents a 'weight' for that neuron, which is a crucial value in AI learning. The RPU is designed to change its resistance in a random, or 'stochastic', and 'non-linear' way based on electrical signals applied to its two terminals (Claim 1). This change in resistance allows the RPU to 'locally perform a data storage operation' (Claim 3) for the neural network's training. Crucially, it also 'locally performs a data processing operation' (Claim 1) using this changed resistance. For example, in a system learning to recognize images, each RPU could adjust its resistance based on input signals, effectively 'remembering' a part of the image, and then use that 'memory' to help process new images, all within the same tiny component.",
  "what_it_does_not_cover": [
    "Does not cover RPUs that use more than two terminals for their operation.",
    "Does not cover RPUs where the resistance changes in a predictable, non-stochastic manner.",
    "Does not cover RPUs where the change in resistance is a simple linear relationship, rather than a non-linear one (Claim 1).",
    "Does not cover systems where data storage and processing are handled by separate, distinct components, rather than locally within the RPU (Claim 3).",
    "Does not cover RPUs that use encoding methods other than stochastic pulse sequences or magnitude modulation for input signals (Claims 6 and 7)."
  ],
  "filed": "2015-10-20",
  "granted": "2019-04-02",
  "expires": "2035-10-20",
  "status": "active",
  "holder": "International Business Machines",
  "holder_url": "https://patentbrief.org/company/international-business-machines",
  "inventors": [
    {
      "name": "Yurii Vlasov",
      "url": "https://patentbrief.org/inventor/yurii-vlasov"
    },
    {
      "name": "Seyoung Kim",
      "url": "https://patentbrief.org/inventor/seyoung-kim"
    },
    {
      "name": "Tayfun Gokmen",
      "url": "https://patentbrief.org/inventor/tayfun-gokmen"
    }
  ],
  "times_cited": 11,
  "tags": [
    "semiconductors",
    "ai_ml",
    "consumer_electronics",
    "software"
  ],
  "abstract": "Embodiments are directed to a two-terminal resistive processing unit (RPU) having a first terminal, a second terminal and an active region. The active region effects a non-linear change in a conduction state of the active region based on at least one first encoded signal applied to the first terminal and at least one second encoded signal applied to the second terminal. The active region is configured to locally perform a data storage operation of a training methodology based at least in part on the non-linear change in the conduction state. The active region is further configured to locally perform a data processing operation of the training methodology based at least in part on the non-linear change in the conduction state.",
  "url": "https://patentbrief.org/patent/us/10248907/resistive-processing-unit",
  "markdown_url": "https://patentbrief.org/patent/us/10248907/resistive-processing-unit/md",
  "google_patents_url": "https://patents.google.com/patent/US10248907",
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}