Intel corporation (20240221756). END-TO-END NEUROMORPHIC ACOUSTIC PROCESSING simplified abstract
Contents
- 1 END-TO-END NEUROMORPHIC ACOUSTIC PROCESSING
- 1.1 Organization Name
- 1.2 Inventor(s)
- 1.3 END-TO-END NEUROMORPHIC ACOUSTIC PROCESSING - A simplified explanation of the abstract
- 1.4 Simplified Explanation
- 1.5 Key Features and Innovation
- 1.6 Potential Applications
- 1.7 Problems Solved
- 1.8 Benefits
- 1.9 Commercial Applications
- 1.10 Prior Art
- 1.11 Frequently Updated Research
- 1.12 Questions about Neuromorphic Processing Devices
- 1.13 Original Abstract Submitted
END-TO-END NEUROMORPHIC ACOUSTIC PROCESSING
Organization Name
Inventor(s)
Kuba Tomasz Lopatka of Gdansk (PL)
Katarzyna J. Kaszuba-miotke of Gdynia (PL)
Karol Jan Polec of Gdansk (PL)
END-TO-END NEUROMORPHIC ACOUSTIC PROCESSING - A simplified explanation of the abstract
This abstract first appeared for US patent application 20240221756 titled 'END-TO-END NEUROMORPHIC ACOUSTIC PROCESSING
Simplified Explanation
This patent application describes a neuromorphic processing device that uses input spikes generated from acoustic signal data to perform tasks like acoustic recognition using a spiking neural network.
- The device includes a spike generator that creates input spikes based on data from a microphone.
- A neuromorphic compute block implements a spiking neural network, receiving the input spikes and generating output spikes for tasks like acoustic recognition.
Key Features and Innovation
- Spike generator creates input spikes from acoustic signal data.
- Neuromorphic compute block implements a spiking neural network for processing.
- Output spikes are generated based on the input spikes for tasks like acoustic recognition.
Potential Applications
- Acoustic recognition tasks
- Speech recognition
- Sound classification
Problems Solved
- Efficient processing of acoustic signal data
- Real-time recognition tasks
- Mimicking biological neural networks for improved performance
Benefits
- Faster processing of acoustic data
- Improved accuracy in recognition tasks
- Low power consumption compared to traditional methods
Commercial Applications
Neuromorphic processing devices can be used in various industries such as:
- Speech recognition software
- Acoustic monitoring systems
- IoT devices for sound detection
Prior Art
Readers can explore prior research on neuromorphic computing, spiking neural networks, and acoustic signal processing to understand the background of this technology.
Frequently Updated Research
Stay updated on advancements in neuromorphic computing, artificial intelligence, and signal processing to see how this technology evolves.
Questions about Neuromorphic Processing Devices
How do neuromorphic processing devices differ from traditional computing devices?
Neuromorphic processing devices mimic the structure and function of biological neural networks, enabling efficient and parallel processing compared to traditional sequential computing.
What are the potential limitations of using spiking neural networks for acoustic recognition tasks?
Spiking neural networks may require complex training algorithms and large datasets to achieve high accuracy in acoustic recognition tasks.
Original Abstract Submitted
a neuromorphic processing device includes a spike generator including hardware to generate a set of input spikes based on acoustic signal data generated by a microphone of a computing device. the neuromorphic processing device further includes a neuromorphic compute block to implement a spiking neural network (snn), receive the set of input spikes as an input to the snn, and generate a set of output spikes from the snn based on the input. a result for an acoustic recognition task may be determined based on the set of output spikes.
