# US 12,766,893 B2 · Neural network circuit for vehicle sensor signal processing > Agents: this data is available as JSON from the patentgrep API and MCP server — see https://patentgrep.com/llms.txt - Number: US 12,766,893 B2 - Document ID: US12766893B2 - Status: Granted - Kind: B2 - Application number: 18/418,212 - Filed: 2024-01-19 - Published: 2025-07-24 - Granted: 2026-10-20 - Earliest claimed: 2024-01-19 - Assignees: PolyN Technology Limited - Applicants: PolyN Technology Limited - Inventors: Dmitrii Godovskii; Boris Maslov; Aleksandrs Timofejevs - Web page: https://patentgrep.com/patent/US12766893B2 ## CPC - B60C 23/0455: {Transmission control of wireless signals} - G06N 3/10: Interfaces, programming languages or software development kits, e.g. for simulating neural networks - B60C: VEHICLE TYRES; TYRE INFLATION; TYRE CHANGING; CONNECTING VALVES TO INFLATABLE ELASTIC BODIES IN GENERAL; DEVICES OR ARRANGEMENTS RELATED TO TYRES - G06N: COMPUTING ARRANGEMENTS BASED ON SPECIFIC COMPUTATIONAL MODELS ## Abstract Systems, devices, integrated circuits, and methods are directed to on-vehicle data processing using analog hardware realization of neural networks. A vehicle obtains a temporal sequence of sensor data samples that is collected by a sensor system including a tire pressure sensor and/or a three-axis accelerometer. The sensor system is physically coupled to a tire of a vehicle. The temporal sequence of sensor data samples is converted into a plurality of first parallel data items, which is applied as a plurality of first inputs to a neural network circuit. The neural network circuit generates one or more output data items based on the plurality of first parallel data items. The one or more output data items indicate a condition of the road, the vehicle, or a component of the vehicle. ## Claims ### Claim 1 A method for processing vehicle data, comprising: obtaining a temporal sequence of sensor data samples that is collected by a sensor, wherein: the sensor is physically coupled to a tire of a vehicle and includes a tire pressure sensor and an accelerometer; and the temporal sequence of sensor data samples includes a temporal sequence of pressure data samples collected by the tire pressure sensor; obtaining a temporal sequence of motion data samples that is collected by the accelerometer of the vehicle; converting the temporal sequence of sensor data samples into a plurality of first parallel data items; converting the temporal sequence of motion data samples into a plurality of second parallel data items; applying the plurality of first parallel data items to a plurality of first inputs of a neural network circuit; applying the plurality of second parallel data items to a plurality of second inputs of the neural network circuit; and generating, by the neural network circuit, one or more output data items based on the plurality of first parallel data items and the plurality of second parallel data items, the one or more output data items indicating a condition of a road, the vehicle, or a component of the vehicle. ### Claim 2 (depends on 1) The method of claim 1, further comprising: communicating the one or more output data items via a wireless communication link, wherein a data size of the one or more output data items is smaller than a size of the plurality of first parallel data items. ### Claim 3 (depends on 2) The method of claim 2, wherein the wireless communication link is established in accordance with a Bluetooth Low Energy (BLE) protocol or a low power device 433 MHz (LPD433) protocol. ### Claim 4 (depends on 1) The method of claim 1, wherein the component includes one of: a wheel hub, a suspension element, a shock absorber, and a frame. ### Claim 5 An electronic device, comprising: a sensor that is a tire pressure sensor or an accelerometer, wherein the sensor is physically coupled to a tire of a vehicle and configured to collect a temporal sequence of sensor data samples used to provide a plurality of first parallel data items; and a neural network circuit coupled to the sensor, wherein: the neural network circuit is configured to receive the plurality of first parallel data items via a plurality of first inputs and generate one or more output data items based on the plurality of first parallel data items; the one or more output data items indicate a condition of a road, the vehicle, or a component of the vehicle; and the neural network circuit further comprises: a digital-to-analog converter (DAC) configured to receive the plurality of first parallel data items via the plurality of first inputs and convert the plurality of first parallel data items into a plurality of analog input signals; a neural network core coupled to the DAC, the neural network core configured to convert the plurality of analog input signals to one or more analog output signals; and an analog-to-digital converter (ADC) coupled to the neural network core, the ADC configured to convert the one or more analog output signals to the one or more output data items. ### Claim 6 (depends on 5) The electronic device of claim 5, further comprising one or more of: a plurality of latches for holding the plurality of first parallel data items concurrently; and a wireless transceiver coupled to the neural network circuit, the wireless transceiver configured to transmit a wireless signal carrying the one or more output data items over a wireless communication link. ### Claim 7 (depends on 5) The electronic device of claim 5, wherein the neural network circuit is configured to implement a convolutional neural network (CNN), a recurrent neural network (RNN), a transformer, or an autoencoder. ### Claim 8 (depends on 5) The electronic device of claim 5, wherein a neural network corresponding to the neural network circuit is trained to identify a plurality of sensor data patterns via the one or more output data items, and the one or more output data items are generated to identify a new pattern of sensor data samples distinct from the plurality of sensor data patterns. ### Claim 9 A method for processing vehicle data, comprising: measuring, by a sensor, a temporal sequence of sensor data samples within a first temporal window having a temporal width and according to a sampling rate, wherein the sensor is physically coupled to a tire of a vehicle and includes a tire pressure sensor and/or an accelerometer; obtaining the temporal sequence of sensor data samples collected by the sensor; converting the temporal sequence of sensor data samples into a plurality of first parallel data items; applying the plurality of first parallel data items to a plurality of first inputs of a neural network circuit; and generating, by the neural network circuit, one or more output data items based on the plurality of first parallel data items, wherein: the one or more output data items correspond to the first temporal window; and the one or more output data items indicate a condition of a road, the vehicle, or a component of the vehicle. ### Claim 10 (depends on 9) The method of claim 9, further comprising: updating the one or more output data items based on a second sequence of sensor data samples that is collected during a second temporal window, wherein the second temporal window immediately follows the first temporal window. ### Claim 11 (depends on 10) The method of claim 10, wherein a last sample of the first temporal window immediately precedes a first sample of the second temporal window. ### Claim 12 (depends on 10) The method of claim 10, wherein a last sample of the first temporal window is separated from a first sample of the second temporal window by at least one sample. ### Claim 13 (depends on 10) The method of claim 10, wherein the first temporal window partially overlaps the second temporal window by a positive number of samples. ### Claim 14 An electronic device, comprising: a sensor that is a tire pressure sensor or an accelerometer, wherein the sensor is physically coupled to a tire of a vehicle and configured to collect a temporal sequence of sensor data samples used to provide a plurality of first parallel data items; and a neural network circuit that is coupled to the sensor and includes a plurality of operational amplifiers and a plurality of resistors, wherein: each amplifier of the neural network circuit forms a respective neuron circuit with a subset of resistors to implement a respective neuron of a neural network; resistances of the plurality of resistors depend on weights associated with neuron inputs of the respective neuron of the neural network; the neural network circuit is configured to receive the plurality of first parallel data items via a plurality of first inputs and generate one or more output data items based on the plurality of first parallel data items; and the one or more output data items indicate a condition of a road, the vehicle, or a component of the vehicle. ### Claim 15 (depends on 14) The electronic device of claim 14, wherein at least a subset of the plurality of resistors is selected from a crossbar array of resistive elements having a plurality of word lines, a plurality of bit lines, and a plurality of resistive elements, and wherein each resistive element is located at a cross point of, and electrically coupled between, a respective word line and a respective bit line. ### Claim 16 (depends on 14) The electronic device of claim 14, wherein one or more of the plurality of resistors are variable resistors configured to implement one or more layers of the neural network, and have variable resistances that are adaptively adjusted for the sensor of the vehicle. ### Claim 17 (depends on 16) The electronic device of claim 16, wherein the one or more layers include a plurality of successive layers that are coupled to an output of the neural network. ### Claim 18 (depends on 16) The electronic device of claim 16, wherein the neural network has a total number of neural layers, the one or more layers have a first number of layers, and a ratio of the first number to the total number is less than a threshold value.