17940845. Artificial Neural Network Computation using Integrated Circuit Devices having Analog Inference Capability simplified abstract (Micron Technology, Inc.)
Contents
- 1 Artificial Neural Network Computation using Integrated Circuit Devices having Analog Inference Capability
- 1.1 Organization Name
- 1.2 Inventor(s)
- 1.3 Artificial Neural Network Computation using Integrated Circuit Devices having Analog Inference Capability - A simplified explanation of the abstract
- 1.4 Simplified Explanation
- 1.5 Potential Applications
- 1.6 Problems Solved
- 1.7 Benefits
- 1.8 Potential Commercial Applications
- 1.9 Unanswered Questions
- 1.10 Original Abstract Submitted
Artificial Neural Network Computation using Integrated Circuit Devices having Analog Inference Capability
Organization Name
Inventor(s)
Artificial Neural Network Computation using Integrated Circuit Devices having Analog Inference Capability - A simplified explanation of the abstract
This abstract first appeared for US patent application 17940845 titled 'Artificial Neural Network Computation using Integrated Circuit Devices having Analog Inference Capability
Simplified Explanation
The patent application describes a method of artificial neural network computations using an integrated circuit device. Here is a simplified explanation of the abstract:
- Receiving image data with pixel values
- Generating a column of inputs for artificial neurons from the pixel values
- Identifying a region of memory cells with programmed threshold voltages representing a weight matrix
- Instructing voltage drivers to apply voltages to the memory cells based on the inputs
- Obtaining a first column of data from multiplication and accumulation operations on the weight matrix and inputs
- Applying activation functions to generate a second column of data representing neuron outputs
Potential Applications
This technology can be applied in various fields such as image recognition, pattern recognition, and machine learning algorithms.
Problems Solved
This technology helps in improving the efficiency and accuracy of artificial neural network computations, especially in tasks involving large datasets and complex patterns.
Benefits
The benefits of this technology include faster processing speeds, reduced energy consumption, and enhanced performance in neural network applications.
Potential Commercial Applications
- Improving image recognition software for security systems
- Enhancing machine learning algorithms for data analysis in various industries
Unanswered Questions
How does this technology compare to traditional neural network computation methods?
This article does not provide a direct comparison between this innovative method and traditional approaches in artificial neural network computations.
What are the limitations of implementing this technology in real-world applications?
The article does not address the potential challenges or constraints faced when integrating this technology into practical systems.
Original Abstract Submitted
A method of artificial neural network computations, including: receiving image data having pixel values; generating, from the pixel values, a column of inputs to a set of artificial neurons; identifying a region of memory cells of the integrated circuit device having threshold voltages programmed to represent a weight matrix for the set of artificial neurons; instructing voltage drivers in the integrated circuit device to apply voltages to the region of memory cells according to the column of inputs; obtaining, based on the region of memory cells responsive to the applied voltages, a first column of data from an operation of multiplication and accumulation applied on the weight matrix and the column of inputs; and applying activation functions of the set of artificial neurons to the first column of data to generate a second column of data representative of outputs of the set of artificial neuron.
