Intel corporation (20240118992). SYSTEMS, APPARATUS, AND METHODS TO DEBUG ACCELERATOR HARDWARE simplified abstract
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 20240118992 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, used for executing artificial intelligence computational workloads. The apparatus includes a core for executing executable code based on a machine-learning model, as well as debug circuitry to detect breakpoints associated with the model and stop the execution of code for debugging purposes.
- Core for executing executable code based on a machine-learning model
- Debug circuitry to detect breakpoints and stop code execution for debugging
- Output data such as data input, data output, and breakpoints for debugging purposes
Potential Applications
The technology can be applied in various industries where hardware accelerators are used for executing artificial intelligence computational workloads, such as autonomous vehicles, healthcare diagnostics, and financial analysis.
Problems Solved
1. Efficient debugging of hardware accelerators used for artificial intelligence workloads. 2. Improved performance and reliability of neural network accelerators through effective debugging mechanisms.
Benefits
1. Enhanced accuracy and reliability of artificial intelligence computations. 2. Streamlined development and testing processes for hardware accelerators. 3. Faster identification and resolution of issues in machine-learning models.
Potential Commercial Applications
"Debugging Hardware Accelerators for AI Workloads" can find commercial applications in industries such as autonomous vehicles, healthcare, finance, and robotics for optimizing the performance of neural network accelerators.
Possible Prior Art
One possible prior art could be the use of software-based debugging tools for identifying and resolving issues in machine-learning models and hardware accelerators. Additionally, existing patents may cover specific aspects of debugging mechanisms for hardware accelerators in the context of artificial intelligence workloads.
Unanswered Questions
How does this technology compare to existing hardware debugging solutions for neural network accelerators?
This article does not provide a direct comparison with existing hardware debugging solutions for neural network accelerators. It would be beneficial to understand the unique features and advantages of this technology in comparison to other solutions currently available in the market.
What impact does this technology have on the overall development time and cost of hardware accelerators for AI workloads?
The article does not address the potential impact of this technology on the development time and cost of hardware accelerators for AI workloads. Understanding the cost-effectiveness and efficiency improvements brought about by this innovation would be crucial for businesses considering its adoption.
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.
