20240048152. WEIGHT PROCESSING FOR A NEURAL NETWORK simplified abstract (Arm Limited)
Contents
WEIGHT PROCESSING FOR A NEURAL NETWORK
Organization Name
Inventor(s)
John Wakefield Brothers, Iii of Calistoga CA (US)
WEIGHT PROCESSING FOR A NEURAL NETWORK - A simplified explanation of the abstract
This abstract first appeared for US patent application 20240048152 titled 'WEIGHT PROCESSING FOR A NEURAL NETWORK
Simplified Explanation
The abstract describes a system and method for processing data for a neural network. The system includes memory to receive data bits defining a kernel of weights and a data processing unit. The data processing unit receives bits defining a kernel of weights for the neural network, generates a set of mask bits indicating the presence of zero-valued or non-zero value weights in the kernel, and transmits the non-zero value weights and the set of mask bits for storage.
- The system processes data for a neural network.
- It uses memory to store data bits defining a kernel of weights.
- The data processing unit receives bits defining a kernel of weights.
- It generates a set of mask bits indicating the presence of zero-valued or non-zero value weights in the kernel.
- The non-zero value weights and the set of mask bits are transmitted for storage.
Potential applications of this technology:
- Neural network training and inference
- Image recognition and classification
- Natural language processing
- Speech recognition
- Autonomous vehicles
- Robotics
Problems solved by this technology:
- Efficient storage and processing of neural network weights
- Reduction of memory requirements
- Optimization of neural network performance
Benefits of this technology:
- Improved efficiency in neural network processing
- Faster training and inference times
- Reduced memory footprint
- Enhanced performance and accuracy in various applications
Original Abstract Submitted
systems and methods for processing data for a neural network are described. the system comprises non-transitory memory configured to receive data bits defining a kernel of weights, the data bits being suitable for processing input data; and a data processing unit, configured to: receive bits defining a kernel of weights for the neural network, the kernel of weights comprising one or more non-zero value weights and one or more zero-valued weights; generate a set of mask bits, a position of each bit in the set of mask bits corresponds to a position within the kernel of weights and the value of each bit indicates whether a weight in the corresponding position is a zero-valued weight or a non-zero value weight; and transmit the non-zero value weights and the set of mask bits for storage, the non-zero value weights and the set of mask bits represent the kernel of weights.
