# US 2026/0100186 A1 · Sensor-processing systems including neuromorphic integrated circuits and methods thereof > Agents: this data is available as JSON from the patentgrep API and MCP server — see https://patentgrep.com/llms.txt - Number: US 2026/0100186 A1 - Document ID: US20260100186A1 - Status: Published application - Kind: A1 - Application number: 19/417,011 - Filed: 2025-12-11 - Published: 2026-04-09 - Earliest claimed: 2018-08-01 - Assignees: Syntiant - Applicants: Syntiant - Inventors: Kurt F. Busch; Jeremiah H. Holleman, III; Pieter Vorenkamp; Stephen W. Bailey; David Christopher Garrett - Web page: https://patentgrep.com/patent/US20260100186A1 ## CPC - G06N 5/046: Forward inferencing; Production systems - G10L 15/16: using artificial neural networks - G06N 3/0495: Quantised networks; Sparse networks; Compressed networks - G06N 3/0499: Feedforward networks - G06N 3/063: using electronic means - G06N 3/08: Learning methods - G06N 3/09: Supervised learning - G10L 15/02: Feature extraction for speech recognition; Selection of recognition unit - G10L 15/22: Procedures used during a speech recognition process, e.g. man-machine dialogue - G10L 17/00: Speaker identification or verification techniques - G10L 25/78: Detection of presence or absence of voice signals (switching of direction of transmission by voice frequency in two-way loud-speaking telephone systems H04M9/10) - G10L 2015/088: {Word spotting} - G06N: COMPUTING ARRANGEMENTS BASED ON SPECIFIC COMPUTATIONAL MODELS - G10L: SPEECH ANALYSIS TECHNIQUES OR SPEECH SYNTHESIS; SPEECH RECOGNITION; SPEECH OR VOICE PROCESSING TECHNIQUES; SPEECH OR AUDIO CODING OR DECODING ## Abstract Disclosed is a sensor-processing system including, in some embodiments, a sensor, one or more sample pre-processing modules, one or more sample-processing modules, one or more neuromorphic integrated circuits (“ICs”), and a microcontroller. The one or more sample pre-processing modules are configured to process raw sensor data for use in the sensor-processing system. The one or more sample-processing modules are configured to process pre-processed sensor data including extracting features from the pre-processed sensor data. Each of the neuromorphic ICs includes at least one neural network configured to arrive at actionable decisions of the neural network from the features extracted from the pre-processed sensor data. The microcontroller includes a CPU along with memory including instructions for operating the sensor-processing system. In some embodiments, the sensor is a pulse-density modulation (“PDM”) microphone, and the sensor-processing system is configured for keyword spotting. Also disclosed are methods of such a keyword spotting sensor-processing system. ## Claims ### Claim 1 A sensor-processing system, comprising: a sensor; one or more sample pre-processing modules configured to process raw sensor data for use in the sensor-processing system; one or more sample-processing modules configured to process pre-processed sensor data including extracting features from the pre-processed sensor data; one or more neuromorphic ICs, each neuromorphic IC including at least one neural network configured to arrive at actionable decisions of the neural network from the features extracted from the pre-processed sensor data; and a microcontroller including at least one central-processing unit (“CPU”) along with memory including instructions for operating the sensor-processing system. ### Claim 2 (depends on 1) The sensor-processing system of claim 1, further comprising a sample holding tank configured to at least temporarily store pre-processed sensor data for subsequent or repeated use in the sensor-processing system. ### Claim 3 (depends on 1) The sensor-processing system of claim 1, further comprising a feature store configured to at least temporarily store the features extracted from the pre-processed sensor data for the one or more neuromorphic ICs. ### Claim 4 (depends on 1) The sensor-processing system of claim 1, wherein the sensor-processing system includes a single neuromorphic IC including a single neural network configured as a classifier. ### Claim 5 (depends on 1) The sensor-processing system of claim 1, wherein the sensor-processing system includes at least a first neuromorphic IC including a relatively larger, primary neural network and a second neuromorphic IC including a relatively smaller, secondary neural network; and wherein the primary neural network is configured to power on and operate on the features extracted from the pre-processed sensor data after the secondary neural network arrives at an actionable decision on the features extracted from the pre-processed sensor data, thereby lowering power consumption of the sensor-processing multi-chip. ### Claim 6 (depends on 1) The sensor-processing system of claim 1, wherein the sensor is an analog or digital microphone, an accelerometer, a gyroscope, a magnetometer, a tilt sensor, a temperature sensor, a humidity sensor, a barometer, a proximity sensor, a light sensor, an infrared sensor, a color sensor, a pressure sensor, a touch sensor, a flow sensor, a level sensor, an ultrasonic sensor, a smoke sensor, a gas sensor, an alcohol sensor, or a combination thereof. ### Claim 7 (depends on 1) The sensor-processing system of claim 1, wherein the sensor is a pulse-density modulation (“PDM”) microphone, and the one or more sample pre-processing modules include a PDM decimation module configured to decimate audio samples from the PDM microphone to a baseband audio sampling rate for use in the sensor-processing system; and wherein the one or more sample-processing modules include a time domain-processing module and a frequency domain-processing module configured to extracting features from decimated audio samples. ### Claim 8 (depends on 7) The sensor-processing system of claim 7, wherein the sensor-processing system is configured as a keyword spotter; and wherein the features are one or more signals in a time domain, a frequency domain, or both the time and frequency domains characteristic of keywords the one or more neural networks are trained to recognize. ### Claim 9 A method of conditional neural network operation in a sensor-processing system upon detection of a credible signal, comprising: operating a pulse-density modulation (“PDM”) microphone, a PDM decimation module, a time domain-processing module, and a frequency domain-processing module, wherein operating the time domain-processing module and the frequency domain-processing module includes identifying one or more signals of an audio sample in a time domain or a frequency domain if the one or more signals are present; and powering on and operating the neural network if the one or more signals are present to determine if the one or more signals includes a keyword. ### Claim 10 (depends on 9) The method of claim 9, further comprising: pulling the audio sample from a sample holding tank to either: confirm the one or more signals includes a keyword; or process the audio sample via an alternative method. ### Claim 11 A method of conditional neural network operation in a sensor-processing system upon detection of a credible keyword, comprising: operating a pulse-density modulation (“PDM”) microphone, a PDM decimation module, a time domain-processing module, and a frequency domain-processing module, wherein operating the time domain-processing module and the frequency domain-processing module includes identifying one or more signals of an audio sample in a time domain or a frequency domain if the one or more signals are present; powering on and operating a lower-powered secondary neural network if the one or more signals are present to determine if the one or more signals includes a keyword; and powering on and operating a larger, higher-powered primary neural network if the one or more signals include a keyword, wherein the primary neural network confirms the one or more signals includes a keyword. ### Claim 12 (depends on 11) The method of claim 11, further comprising: pulling the audio sample from a sample holding tank to either: confirm the one or more signals includes a keyword; or process the audio sample via an alternative method. ### Claim 13 A method of intervallically operating a neural network of a sensor-processing system, comprising: operating a pulse-density modulation (“PDM”) microphone, a PDM decimation module, a time domain-processing module, and a frequency domain-processing module, wherein operating the time domain-processing module and the frequency domain-processing module includes identifying one or more signals of an audio sample in a time domain or a frequency domain if the one or more signals are present; and powering on and operating the neural network every frame of a first pre-determined frequency during the processing of the audio sample to determine if the one or more signals are present and if the one or more signals includes a keyword. ### Claim 14 (depends on 13) The method of claim 13, further comprising: operating the neural network at a second pre-determined frequency if the one or more signals includes a keyword to facilitate capture of any subsequent keywords, wherein the second pre-determined frequency is greater than the first pre-determined frequency. ### Claim 15 (depends on 13) The method of either claim 13, further comprising: pulling the audio sample from a sample holding tank to either: confirm the one or more signals includes a keyword; or process the audio sample via an alternative method. ### Claim 16 A method of microphone-mode switching for a sensor-processing system, comprising: operating a pulse-density modulation (“PDM”) microphone in a lower-frequency mode to conserve power; operating a time domain-processing module and a frequency domain-processing module, the operations including: extracting features from an audio sample; and determining if one or more signals are present in a time domain or a frequency domain; operating the PDM microphone in a higher-frequency mode for better signal-to-noise ratio if the one or more signals are present; and operating a PDM decimation module in accordance with either the lower-frequency mode or the higher-frequency mode to format the audio sample for use in a sensor-processing system. ### Claim 17 (depends on 16) The method of claim 16, further comprising: powering on and operating a neural network to determine if the features extracted from the audio sample include one or more keywords. ### Claim 18 (depends on 17) The method of claim 17, further comprising: pulling the audio sample from a sample holding tank to either: confirm the features extracted from the audio sample include one or more keywords; or process the audio sample via an alternative method. ### Claim 19 A method of speaker identification for a sensor-processing system, comprising: operating a pulse-density modulation (“PDM”) microphone, a PDM decimation module, a time domain-processing module, and a frequency domain-processing module, wherein operating the time domain-processing module and the frequency domain-processing module includes extracting one or more features of an audio sample; and powering on and operating a neural network to determine if the one or more features are characteristic of an assigned speaker, wherein the sensor-processing system is configured to continue extracting features from audio samples and operating the neural network to identify keywords if a speaker is identified as the assigned speaker. ### Claim 20 A method for a sample holding tank of a sensor-processing system, comprising: operating a pulse-density modulation (“PDM”) microphone and a PDM decimation module to format an audio sample for use in the sensor-processing system; sending the audio sample to both the holding tank and one or more sample-processing modules; operating a time domain-processing module and a frequency domain-processing module to extract features from the audio sample; operating a neural network to determine if the features extracted from the audio sample include one or more keywords; and pulling the audio sample from the sample holding tank and sending the audio sample to the one or more sample-processing modules for additional but different sample processing to confirm the features extracted from the audio sample include the one or more keywords.