Intel corporation (20240104916). DEEP LEARNING INFERENCE EFFICIENCY TECHNOLOGY WITH EARLY EXIT AND SPECULATIVE EXECUTION simplified abstract
Contents
- 1 DEEP LEARNING INFERENCE EFFICIENCY TECHNOLOGY WITH EARLY EXIT AND SPECULATIVE EXECUTION
- 1.1 Organization Name
- 1.2 Inventor(s)
- 1.3 DEEP LEARNING INFERENCE EFFICIENCY TECHNOLOGY WITH EARLY EXIT AND SPECULATIVE EXECUTION - 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 Unanswered Questions
- 1.11 Original Abstract Submitted
DEEP LEARNING INFERENCE EFFICIENCY TECHNOLOGY WITH EARLY EXIT AND SPECULATIVE EXECUTION
Organization Name
Inventor(s)
Haim Barad of Zichron Yaakov (IL)
Barak Hurwitz of Kibbutz Alonim (IL)
Uzi Sarel of Zichron-Yaakov (IL)
DEEP LEARNING INFERENCE EFFICIENCY TECHNOLOGY WITH EARLY EXIT AND SPECULATIVE EXECUTION - A simplified explanation of the abstract
This abstract first appeared for US patent application 20240104916 titled 'DEEP LEARNING INFERENCE EFFICIENCY TECHNOLOGY WITH EARLY EXIT AND SPECULATIVE EXECUTION
Simplified Explanation
The technology described in the patent application processes an inference workload in a neural network by selectively bypassing processing of certain layers based on exit criteria and speculatively initiating processing while the exit determination is pending. It also masks batches from processing in certain layers when dealing with multiple batches.
- Technology processes inference workload in a neural network
- Selectively bypasses processing of certain layers based on exit criteria
- Speculatively initiates processing while exit determination is pending
- Masks batches from processing in certain layers when dealing with multiple batches
Potential Applications
This technology could be applied in various fields such as:
- Image recognition
- Natural language processing
- Autonomous vehicles
- Medical diagnostics
Problems Solved
The technology addresses the following issues:
- Efficient processing of inference workloads
- Reduction of data dependent branch operations
- Improved performance of neural networks
Benefits
The benefits of this technology include:
- Faster inference processing
- Enhanced efficiency in neural network operations
- Optimal resource utilization
Potential Commercial Applications
Potential commercial applications of this technology could include:
- AI-powered software applications
- Cloud computing services
- Robotics and automation systems
Possible Prior Art
One possible prior art in this field is the use of parallel processing techniques in neural networks to improve efficiency and performance.
Unanswered Questions
How does this technology compare to existing methods in terms of speed and accuracy of inference processing?
The article does not provide a direct comparison with existing methods in terms of speed and accuracy of inference processing.
What are the potential limitations or challenges in implementing this technology in real-world applications?
The article does not discuss the potential limitations or challenges in implementing this technology in real-world applications.
Original Abstract Submitted
systems, apparatuses and methods may provide for technology that processes an inference workload in a first subset of layers of a neural network that prevents or inhibits data dependent branch operations, conducts an exit determination as to whether an output of the first subset of layers satisfies one or more exit criteria, and selectively bypasses processing of the output in a second subset of layers of the neural network based on the exit determination. the technology may also speculatively initiate the processing of the output in the second subset of layers while the exit determination is pending. additionally, when the inference workloads include a plurality of batches, the technology may mask one or more of the plurality of batches from processing in the second subset of layers.
