Qualcomm incorporated (20240112090). CONCURRENT OPTIMIZATION OF MACHINE LEARNING MODEL PERFORMANCE simplified abstract
Contents
- 1 CONCURRENT OPTIMIZATION OF MACHINE LEARNING MODEL PERFORMANCE
- 1.1 Organization Name
- 1.2 Inventor(s)
- 1.3 CONCURRENT OPTIMIZATION OF MACHINE LEARNING MODEL PERFORMANCE - 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 Answer
- 1.11 Answer
- 1.12 Original Abstract Submitted
CONCURRENT OPTIMIZATION OF MACHINE LEARNING MODEL PERFORMANCE
Organization Name
Inventor(s)
Serag Gadelrab of Markham (CA)
James Lyall Esliger of Tillsonburg (CA)
Meghal Varia of North York (CA)
Alwyn Dos Remedios of Maple (CA)
CONCURRENT OPTIMIZATION OF MACHINE LEARNING MODEL PERFORMANCE - A simplified explanation of the abstract
This abstract first appeared for US patent application 20240112090 titled 'CONCURRENT OPTIMIZATION OF MACHINE LEARNING MODEL PERFORMANCE
Simplified Explanation
The present disclosure provides techniques for concurrently performing inferences using a machine learning model and optimizing parameters used in executing the machine learning model.
- Receiving a request to perform inferences on a data set using a machine learning model and performance metric targets.
- Performing at least a first inference on the data set to meet a specified latency.
- Identifying operational parameters for inference performance based on the machine learning model and computing device properties.
- Applying identified operational parameters to subsequent inferences using the machine learning model.
Potential Applications
This technology can be applied in various fields such as healthcare, finance, e-commerce, and autonomous vehicles for real-time decision-making and optimization of machine learning models.
Problems Solved
1. Efficiently performing inferences while meeting performance metric targets. 2. Optimizing parameters for machine learning models in real-time applications.
Benefits
1. Improved inference performance. 2. Real-time optimization of machine learning models. 3. Enhanced decision-making capabilities.
Potential Commercial Applications
Optimizing machine learning models for real-time applications in industries such as healthcare, finance, e-commerce, and autonomous vehicles.
Possible Prior Art
Prior art may include techniques for optimizing machine learning models or real-time inference systems in various industries. Research papers, patents, or products related to real-time optimization of machine learning models could be considered as prior art.
What are the potential challenges in implementing this technology in real-world applications?
Answer
Some potential challenges in implementing this technology in real-world applications include: 1. Ensuring compatibility with existing systems and infrastructure. 2. Addressing privacy and security concerns related to data used for training and inference processes.
How does this technology compare to traditional methods of optimizing machine learning models?
Answer
This technology offers real-time optimization of machine learning models based on operational parameters, which can lead to improved performance and efficiency compared to traditional methods that may require manual tuning and optimization.
Original Abstract Submitted
certain aspects of the present disclosure provide techniques for concurrently performing inferences using a machine learning model and optimizing parameters used in executing the machine learning model. an example method generally includes receiving a request to perform inferences on a data set using the machine learning model and performance metric targets for performance of the inferences. at least a first inference is performed on the data set using the machine learning model to meet a latency specified for generation of the first inference from receipt of the request. while performing the at least the first inference, operational parameters resulting in inference performance approaching the performance metric targets are identified based on the machine learning model and operational properties of the computing device. the identified operational parameters are applied to performance of subsequent inferences using the machine learning model.
