18392761. METHODS AND APPARATUS TO TILE WALK A TENSOR FOR CONVOLUTION OPERATIONS simplified abstract (Intel Corporation)
Contents
- 1 METHODS AND APPARATUS TO TILE WALK A TENSOR FOR CONVOLUTION OPERATIONS
- 1.1 Organization Name
- 1.2 Inventor(s)
- 1.3 METHODS AND APPARATUS TO TILE WALK A TENSOR FOR CONVOLUTION OPERATIONS - 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 Possible Prior Art
- 1.10 Original Abstract Submitted
METHODS AND APPARATUS TO TILE WALK A TENSOR FOR CONVOLUTION OPERATIONS
Organization Name
Inventor(s)
Yaniv Fais of Tel Aviv TA (IL)
Moshe Maor of Kiryat Mozking Z (IL)
METHODS AND APPARATUS TO TILE WALK A TENSOR FOR CONVOLUTION OPERATIONS - A simplified explanation of the abstract
This abstract first appeared for US patent application 18392761 titled 'METHODS AND APPARATUS TO TILE WALK A TENSOR FOR CONVOLUTION OPERATIONS
Simplified Explanation
The example apparatus described in the abstract is designed to perform a convolution on an input tensor using hardware accelerator circuitry. Here is a simplified explanation of the patent application:
- Parameters generator creates horizontal and vertical hardware execution parameters based on kernel and layer parameters.
- Accelerator interface configures hardware accelerator circuitry with the generated parameters.
- Horizontal Iterator controller monitors completion of the first horizontal iteration of the convolution.
- Vertical Iterator controller monitors completion of the first vertical iteration of the convolution.
Potential Applications
This technology can be applied in various fields such as image processing, signal processing, and machine learning for tasks like feature extraction, pattern recognition, and data analysis.
Problems Solved
1. Speed: By utilizing hardware acceleration, the convolution process can be performed faster compared to traditional software implementations. 2. Efficiency: The hardware execution parameters optimize the convolution process for improved efficiency and resource utilization.
Benefits
1. Faster processing times. 2. Improved efficiency and resource utilization. 3. Enhanced performance for convolution tasks.
Potential Commercial Applications
Optimized hardware accelerators for convolution tasks can be integrated into devices such as smartphones, cameras, autonomous vehicles, and data centers for real-time processing and analysis.
Possible Prior Art
Prior art may include existing hardware accelerators for convolution tasks, software implementations of convolution algorithms, and research papers on optimizing convolution processes using hardware acceleration.
Unanswered Questions
How does this technology compare to existing software-based convolution methods?
This article does not provide a direct comparison between the hardware-accelerated convolution approach and traditional software-based methods. It would be beneficial to understand the performance differences, resource utilization, and efficiency gains of this technology compared to existing solutions.
What are the potential limitations or constraints of implementing this hardware accelerator in different devices or systems?
The article does not address the potential challenges or constraints of integrating this hardware accelerator into various devices or systems. Factors such as power consumption, compatibility with different architectures, and scalability could be important considerations for implementation.
Original Abstract Submitted
An example apparatus to perform a convolution on an input tensor includes a parameters generator to: generate a horizontal hardware execution parameter for a horizontal dimension of the input tensor based on a kernel parameter and a layer parameter; and generate a vertical hardware execution parameter for a vertical dimension of the input tensor based on the kernel parameter and the layer parameter; an accelerator interface to configure a hardware accelerator circuitry based on the horizontal and vertical hardware execution parameters; a horizontal Iterator controller to determine when the hardware accelerator circuitry completes the first horizontal iteration of the convolution; and a vertical Iterator controller to determine when the hardware accelerator circuitry completes the first vertical iteration of the convolution.
