Google llc (20240231819). NEURAL NETWORK COMPUTE TILE simplified abstract
Contents
NEURAL NETWORK COMPUTE TILE
Organization Name
Inventor(s)
Ravi Narayanaswami of San Jose CA (US)
Harshit Khaitan of San Jose CA (US)
Dong Hyuk Woo of San Jose CA (US)
NEURAL NETWORK COMPUTE TILE - A simplified explanation of the abstract
This abstract first appeared for US patent application 20240231819 titled 'NEURAL NETWORK COMPUTE TILE
The abstract describes a computing unit with two memory banks, one for input activations and the other for parameters used in computations. It includes cells with multiply-accumulate (MAC) operators that perform computations using parameters from the second memory bank. A traversal unit controls the input activations provided to the MAC operator for computations associated with data arrays.
- The computing unit has a first memory bank for input activations and a second memory bank for parameters.
- It includes cells with MAC operators that perform computations using parameters from the second memory bank.
- A traversal unit controls the input activations provided to the MAC operator for computations associated with data arrays.
- The MAC operator performs multiply operations on input activations and parameters from the memory bank.
- The computing unit is designed to efficiently perform computations on data arrays.
Potential Applications: - This technology can be used in artificial intelligence systems for deep learning applications. - It can be applied in signal processing for efficient data processing. - The computing unit can enhance performance in scientific computing for complex simulations.
Problems Solved: - Efficient storage and retrieval of input activations and parameters for computations. - Streamlined processing of data arrays with multiply-accumulate operations. - Improved performance and speed in computational tasks.
Benefits: - Faster and more efficient computations. - Enhanced accuracy in data processing. - Optimal utilization of memory resources.
Commercial Applications: Title: Advanced Computing Unit for AI Systems This technology can be utilized in AI systems for various applications such as image recognition, natural language processing, and autonomous vehicles. It can also benefit industries like healthcare, finance, and cybersecurity by improving data analysis and decision-making processes.
Questions about the technology: 1. How does the computing unit optimize memory usage for efficient computations? 2. What are the potential scalability options for this technology in large-scale computing systems?
Frequently Updated Research: Researchers are continually exploring ways to enhance the performance and capabilities of computing units for various applications, including deep learning, scientific computing, and signal processing. Stay updated on the latest advancements in hardware and software integration for improved computational efficiency.
Original Abstract Submitted
a computing unit is disclosed, comprising a first memory bank for storing input activations and a second memory bank for storing parameters used in performing computations. the computing unit includes at least one cell comprising at least one multiply accumulate (“mac”) operator that receives parameters from the second memory bank and performs computations. the computing unit further includes a first traversal unit that provides a control signal to the first memory bank to cause an input activation to be provided to a data bus accessible by the mac operator. the computing unit performs one or more computations associated with at least one element of a data array, the one or more computations being performed by the mac operator and comprising, in part, a multiply operation of the input activation received from the data bus and a parameter received from the second memory bank.
