18487490. SYSTEMS, APPARATUS, AND METHODS TO DEBUG ACCELERATOR HARDWARE simplified abstract (Intel Corporation)
Contents
- 1 SYSTEMS, APPARATUS, AND METHODS TO DEBUG ACCELERATOR HARDWARE
- 1.1 Organization Name
- 1.2 Inventor(s)
- 1.3 SYSTEMS, APPARATUS, AND METHODS TO DEBUG ACCELERATOR HARDWARE - 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
SYSTEMS, APPARATUS, AND METHODS TO DEBUG ACCELERATOR HARDWARE
Organization Name
Inventor(s)
Martin-Thomas Grymel of Leixlip (IE)
David Bernard of Kilcullen (IE)
SYSTEMS, APPARATUS, AND METHODS TO DEBUG ACCELERATOR HARDWARE - A simplified explanation of the abstract
This abstract first appeared for US patent application 18487490 titled 'SYSTEMS, APPARATUS, AND METHODS TO DEBUG ACCELERATOR HARDWARE
Simplified Explanation
The patent application describes methods and apparatus for debugging a hardware accelerator, such as a neural network accelerator, for executing Artificial Intelligence computational workloads. The apparatus includes a core for executing executable code based on a machine-learning model to generate a data output, and debug circuitry coupled to the core. The debug circuitry is configured to detect a breakpoint associated with the machine-learning model, compile executable code based on the model or the breakpoint, and stop the execution of the code in response to the triggering of the breakpoint.
- The apparatus includes a core with input and output for executing executable code based on a machine-learning model.
- Debug circuitry is coupled to the core and is configured to detect breakpoints associated with the model.
- The debug circuitry compiles executable code based on the model or breakpoints and stops code execution when a breakpoint is triggered.
- Data input, data output, and the breakpoint are output for debugging the hardware accelerator.
Potential Applications
This technology can be applied in various industries such as:
- Artificial Intelligence
- Machine Learning
- Hardware Acceleration
Problems Solved
This technology helps in:
- Debugging hardware accelerators efficiently
- Identifying and fixing issues in machine-learning models
- Improving the performance of neural network accelerators
Benefits
The benefits of this technology include:
- Enhanced debugging capabilities for hardware accelerators
- Increased efficiency in identifying and resolving issues in machine-learning models
- Improved overall performance of neural network accelerators
Potential Commercial Applications
Potential commercial applications of this technology include:
- AI hardware development companies
- Tech companies focusing on machine learning
- Research institutions working on neural network accelerators
Possible Prior Art
One possible prior art for this technology could be traditional debugging methods used in software development, which may not be optimized for hardware accelerators like neural network accelerators.
What are the limitations of the current debugging methods for hardware accelerators?
Current debugging methods for hardware accelerators may lack efficiency and specificity in identifying and resolving issues related to machine-learning models and breakpoints.
How does this technology improve the debugging process for hardware accelerators compared to traditional methods?
This technology provides a more targeted approach to debugging by detecting breakpoints associated with machine-learning models and stopping code execution, allowing for more precise identification and resolution of issues.
Original Abstract Submitted
Methods, apparatus, systems, and articles of manufacture are disclosed to debug a hardware accelerator such as a neural network accelerator for executing Artificial Intelligence computational workloads. An example apparatus includes a core with a core input and a core output to execute executable code based on a machine-learning model to generate a data output based on a data input, and debug circuitry coupled to the core. The debug circuitry is configured to detect a breakpoint associated with the machine-learning model, compile executable code based on at least one of the machine-learning model or the breakpoint. In response to the triggering of the breakpoint, the debug circuitry is to stop the execution of the executable code and output data such as the data input, data output and the breakpoint for debugging the hardware accelerator.
