Apple inc. (20240095541). COMPILING OF TASKS FOR STREAMING OPERATIONS AT NEURAL PROCESSOR simplified abstract
Contents
- 1 COMPILING OF TASKS FOR STREAMING OPERATIONS AT NEURAL PROCESSOR
- 1.1 Organization Name
- 1.2 Inventor(s)
- 1.3 COMPILING OF TASKS FOR STREAMING OPERATIONS AT NEURAL PROCESSOR - 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
COMPILING OF TASKS FOR STREAMING OPERATIONS AT NEURAL PROCESSOR
Organization Name
Inventor(s)
Sayyed Karen Khatamifard of Bellevue WA (US)
Thomas G. Anderl of Seattle WA (US)
Alexander J. Kirchhoff of Seattle CA (US)
Dylan H. Rush of Mountlake Terrace WA (US)
Chenfan Sun of SHORELINE WA (US)
Jeffrey D Marker of Pleasant View UT (US)
COMPILING OF TASKS FOR STREAMING OPERATIONS AT NEURAL PROCESSOR - A simplified explanation of the abstract
This abstract first appeared for US patent application 20240095541 titled 'COMPILING OF TASKS FOR STREAMING OPERATIONS AT NEURAL PROCESSOR
Simplified Explanation
The patent application relates to compiling neural network operations into tasks that can be performed in a streaming manner by a neural processor. Tasks associated with multiple layers of the neural network are performed simultaneously in an overlapping manner to improve efficiency. Memory usage is optimized by assigning tasks with similar completion times to the same portion of memory, which can be flushed after completion to make space for new tasks. Tasks can also be combined to reduce the total number of tasks and improve processing efficiency.
- Neural network operations compiled into tasks for streaming processing
- Tasks from multiple layers performed simultaneously in an overlapping manner
- Memory optimization by assigning tasks with similar completion times to the same memory portion
- Flushing memory after tasks completion to make space for new tasks
- Combining multiple tasks to reduce total number of tasks and improve processing efficiency
Potential Applications
This technology can be applied in various fields such as:
- Real-time image and video processing
- Natural language processing
- Autonomous vehicles
- Robotics
Problems Solved
- Efficient memory usage during streaming operations
- Simultaneous processing of tasks from multiple layers of neural networks
- Optimization of neural processor performance
Benefits
- Improved processing efficiency
- Reduced memory usage
- Faster task completion
- Enhanced overall performance of neural processors
Potential Commercial Applications
- AI hardware development companies
- Cloud computing providers
- Autonomous vehicle manufacturers
- Robotics companies
Possible Prior Art
One possible prior art could be the use of parallel processing techniques in neural network operations to improve efficiency and performance.
Unanswered Questions
How does this technology compare to existing methods of neural network processing?
This article does not provide a direct comparison to existing methods of neural network processing. It would be beneficial to understand the specific advantages and disadvantages of this technology compared to traditional methods.
What are the potential limitations or challenges of implementing this technology in real-world applications?
The article does not address any potential limitations or challenges that may arise when implementing this technology in real-world applications. It would be important to consider factors such as scalability, compatibility with existing systems, and potential bottlenecks in processing speed.
Original Abstract Submitted
embodiments relate to compiling neural network operations into tasks that may be performed in a streaming manner by a neural processor. in a streaming operation, a tensor is spatially partitioned, and tasks associated two or more layers of the neural network are performed simultaneously in an overlapping manner. to enable efficient memory usage during streaming operation, a subset of the tasks having completion times close in time are assigned to a same portion of memory in the neural processor during a compilation process. after the tasks assigned to the same portion of the memory is finished, the portion of the memory may be flushed to make space for subsequent tasks. multiple tasks may also be coalesced into a single task to reduce the number of tasks and more efficiently perform the operations at the neural processor.
