Google llc (20240311267). EFFICIENT HARDWARE ACCELERATOR CONFIGURATION EXPLORATION simplified abstract
Contents
- 1 EFFICIENT HARDWARE ACCELERATOR CONFIGURATION EXPLORATION
- 1.1 Organization Name
- 1.2 Inventor(s)
- 1.3 EFFICIENT HARDWARE ACCELERATOR CONFIGURATION EXPLORATION - A simplified explanation of the abstract
- 1.4 Simplified Explanation
- 1.5 Key Features and Innovation
- 1.6 Potential Applications
- 1.7 Problems Solved
- 1.8 Benefits
- 1.9 Commercial Applications
- 1.10 Prior Art
- 1.11 Frequently Updated Research
- 1.12 Questions about Surrogate Neural Network for Hardware Accelerator Performance Prediction and Configuration Determination
- 1.13 Original Abstract Submitted
EFFICIENT HARDWARE ACCELERATOR CONFIGURATION EXPLORATION
Organization Name
Inventor(s)
Amir Yazdanbakhsh of San Jose CA (US)
Sergey Vladimir Levine of Berkeley CA (US)
Aviral Kumar of Berkeley CA (US)
EFFICIENT HARDWARE ACCELERATOR CONFIGURATION EXPLORATION - A simplified explanation of the abstract
This abstract first appeared for US patent application 20240311267 titled 'EFFICIENT HARDWARE ACCELERATOR CONFIGURATION EXPLORATION
Simplified Explanation
The patent application describes methods, systems, and apparatus for training a surrogate neural network to predict the performance of a hardware accelerator on a specific application. This trained network can be used during a search process to determine hardware configurations for application-specific hardware accelerators.
- Surrogate neural network trained to predict hardware accelerator performance
- Used in search process for determining hardware configurations
- Enhances efficiency of hardware accelerator design process
Key Features and Innovation
- Training a surrogate neural network to predict hardware accelerator performance
- Integration of neural networks in hardware accelerator design process
- Improving efficiency and accuracy of hardware accelerator configuration determination
Potential Applications
- Hardware accelerator design and optimization
- Machine learning tasks deployment on hardware accelerators
- Performance prediction for specific applications
Problems Solved
- Enhancing efficiency of hardware accelerator design process
- Improving accuracy of hardware configuration determination
- Streamlining application-specific hardware accelerator development
Benefits
- Faster and more accurate hardware accelerator design process
- Optimized hardware configurations for specific applications
- Enhanced performance prediction capabilities
Commercial Applications
"Surrogate Neural Network for Hardware Accelerator Performance Prediction and Configuration Determination" can be utilized in industries such as:
- Semiconductor manufacturing
- Artificial intelligence hardware development
- Cloud computing infrastructure optimization
Prior Art
Readers interested in prior art related to this technology can explore research on:
- Neural network training for hardware performance prediction
- Hardware accelerator design optimization techniques
Frequently Updated Research
Stay updated on the latest advancements in:
- Surrogate neural network training methods
- Application-specific hardware accelerator development techniques
Questions about Surrogate Neural Network for Hardware Accelerator Performance Prediction and Configuration Determination
How does the surrogate neural network improve the hardware accelerator design process?
The surrogate neural network enhances the efficiency and accuracy of determining hardware configurations for specific applications, streamlining the design process.
What are the potential commercial applications of this technology?
This technology can be applied in industries such as semiconductor manufacturing, AI hardware development, and cloud computing infrastructure optimization.
Original Abstract Submitted
methods, systems, and apparatus, including computer programs encoded on computer storage media, for training a surrogate neural network configured to determine a predicted performance measure of a hardware accelerator having a target hardware configuration on a target application. the trained instance of the surrogate neural network can be used. in addition to or in place of hardware simulation, during a search process for determining hardware configurations for application-specific hardware accelerators. i.e., hardware accelerators on which one or more neural networks can be deployed to perform one or more target machine learning tasks.
