GENERAL PADDING SUPPORT FOR CONVOLUTION ON SYSTOLIC ARRAYS

Organization Name

Inventor(s)

David Alexander Majnemer of Mountain View CA (US)

Blake Alan Hechtman of Mountain View CA (US)

Bjarke Hammersholt Roune of Mountain View CA (US)

GENERAL PADDING SUPPORT FOR CONVOLUTION ON SYSTOLIC ARRAYS - A simplified explanation of the abstract

This abstract first appeared for US patent application 20240232598 titled 'GENERAL PADDING SUPPORT FOR CONVOLUTION ON SYSTOLIC ARRAYS

Simplified Explanation

The patent application describes a method for performing convolutional computations for a neural network on a hardware circuit, involving transferring data between memories and checking memory consistency.

Key Features and Innovation

Method for performing convolutional computations for a neural network on a hardware circuit
Involves transferring data between main memory and scratchpad memory
Checks memory consistency for subsets of data during computations

Potential Applications

This technology can be applied in various fields such as artificial intelligence, machine learning, computer vision, and data processing.

Problems Solved

This technology addresses the need for efficient convolutional computations for neural networks on hardware circuits while ensuring memory consistency.

Benefits

Improved efficiency in performing convolutional computations
Enhanced memory management during neural network operations
Streamlined data processing for complex algorithms

Commercial Applications

This technology can be utilized in AI-powered systems, image recognition software, and data analytics platforms.
It has implications in industries such as healthcare, finance, and autonomous vehicles.

Prior Art

Readers can explore prior research on hardware acceleration for neural networks, memory management in convolutional computations, and optimization techniques for matrix operations.

Frequently Updated Research

Stay updated on advancements in hardware acceleration for neural networks, memory optimization strategies, and innovations in convolutional computation techniques.

Questions about Convolutional Computations

How does this technology improve the efficiency of neural network operations?

This technology enhances efficiency by optimizing memory usage and data transfer between memories, reducing processing time and resource consumption.

What are the potential applications of this method in real-world scenarios?

This method can be applied in various industries such as healthcare, finance, and autonomous vehicles for tasks like image recognition, data analysis, and decision-making processes.

Original Abstract Submitted

methods and systems, including computer programs encoded on a computer storage medium. in one aspect, a method includes the actions of receiving a request to perform convolutional computations for a neural network on a hardware circuit having a matrix computation unit, the request specifying the convolutional computation to be performed on a feature tensor and a filter and padding applied to the feature tensor prior to performing the convolutional computation; and generating instructions that when executed by the hardware circuit cause the hardware circuit to perform operations comprising: transferring feature tensor data from a main memory of the hardware circuit to a scratchpad memory of the hardware circuit; and repeatedly performing the following operations: identifying a current subset of the feature tensor; and determining whether a memory view into the scratchpad memory for the current subset is consistent with a memory view of the current subset in the main memory.