17887183. METHOD AND APPARATUS FOR NEURAL NETWORK OPERATION simplified abstract (Samsung Electronics Co., Ltd.)
Contents
METHOD AND APPARATUS FOR NEURAL NETWORK OPERATION
Organization Name
Inventor(s)
Donghyun Lee of Seongnam-si (KR)
METHOD AND APPARATUS FOR NEURAL NETWORK OPERATION - A simplified explanation of the abstract
This abstract first appeared for US patent application 17887183 titled 'METHOD AND APPARATUS FOR NEURAL NETWORK OPERATION
Simplified Explanation
The abstract describes a neural network operation apparatus that performs a non-linear function in a neural network operation. Here is a simplified explanation of the abstract:
- The apparatus receives input data for the neural network operation.
- It uses a quantized Look Up Table (LUT) that corresponds to a non-linear function in the neural network operation.
- The input data is scaled up based on a scale factor.
- A quantized LUT parameter is extracted from the quantized LUT using the scaled-up input data.
- The apparatus generates an operation result by performing the neural network operation based on the quantized LUT parameter.
Potential applications of this technology:
- Artificial intelligence and machine learning systems
- Image and speech recognition systems
- Natural language processing systems
- Autonomous vehicles and robotics
Problems solved by this technology:
- Efficiently performing non-linear functions in neural network operations
- Reducing the computational complexity of neural network operations
- Improving the speed and efficiency of neural network operations
Benefits of this technology:
- Faster and more efficient neural network operations
- Reduced computational resources required
- Improved accuracy and performance of artificial intelligence systems
Original Abstract Submitted
A neural network operation apparatus may include a receiver configured to receive input data to perform the neural network operation and a quantized Look Up Table (LUT) corresponding to a non-linear function comprised in the neural network operation, and a processor configured to perform scale-up on the input data based on a scale factor, to extract a quantized LUT parameter from the quantized LUT based on scaled-up input data, and to generate an operation result by performing a neural network operation based on the quantized LUT parameter.