Tesla, inc. (20240296330). NEURAL NETWORKS FOR EMBEDDED DEVICES simplified abstract
Contents
NEURAL NETWORKS FOR EMBEDDED DEVICES
Organization Name
Inventor(s)
Forrest Nelson Iandola of San Jose CA (US)
Harsimran Singh Sidhu of Fremont CA (US)
NEURAL NETWORKS FOR EMBEDDED DEVICES - A simplified explanation of the abstract
This abstract first appeared for US patent application 20240296330 titled 'NEURAL NETWORKS FOR EMBEDDED DEVICES
The abstract describes a neural network architecture designed to reduce the processing load for implementing the network, making it suitable for reduced-bit processing devices. The architecture limits the number of bits used for processing to prevent data overflow and may modify the number of bits used to represent inputs and filter masks to ensure the output does not exceed the processor's capacity. Additionally, the network may incorporate a "starconv" structure to balance processing requirements and learn from the context of nearby nodes.
- Neural network architecture designed for reduced-bit processing devices
- Limits the number of bits used for processing to prevent data overflow
- Modifies the number of bits used for inputs and filter masks to avoid exceeding processor capacity
- Incorporates a "starconv" structure to balance processing requirements and learn from nearby nodes
- Suitable for implementing neural networks on devices with limited processing capabilities
Potential Applications: - Implementing neural networks on devices with reduced processing capabilities - Enhancing the efficiency of neural network processing on low-power devices
Problems Solved: - Preventing data overflow during neural network processing - Adapting neural networks for use on reduced-bit processing devices
Benefits: - Improved efficiency of neural network processing on low-power devices - Enhanced performance of neural networks with limited processing capabilities
Commercial Applications: Title: "Efficient Neural Network Architecture for Low-Power Devices" This technology could be utilized in: - Mobile devices - IoT devices - Wearable technology - Edge computing applications
Questions about the technology: 1. How does the "starconv" structure help balance processing requirements in the neural network? 2. What are the specific modifications made to the number of bits used for inputs and filter masks in the architecture?
Original Abstract Submitted
a neural network architecture is used that reduces the processing load of implementing the neural network. this network architecture may thus be used for reduced-bit processing devices. the architecture may limit the number of bits used for processing and reduce processing to prevent data overflow at individual calculations of the neural network. to implement this architecture, the number of bits used to represent inputs at levels of the network and the related filter masks may also be modified to ensure the number of bits of the output does not overflow the resulting capacity of the reduced-bit processor. to additionally reduce the load for such a network, the network may implement a “starconv” structure that permits the incorporation of nearby nodes in a layer to balance processing requirements and permit the network to learn from context of other nodes.
