View source for 18091907. MODELING AND COMPILING TENSOR PROCESSING APPLICATIONS FOR A COMPUTING PLATFORM USING MULTI-LAYER ADAPTIVE DATA FLOW GRAPHS simplified abstract (XILINX, INC.)

=MODELING AND COMPILING TENSOR PROCESSING APPLICATIONS FOR A COMPUTING PLATFORM USING MULTI-LAYER ADAPTIVE DATA FLOW GRAPHS=


==Organization Name==

[[:Category:XILINX, INC.|XILINX, INC.]]


[[Category:XILINX, INC.]]

==Inventor(s)==

[[:Category:Chia-Jui Hsu of Santa Clara CA (US)|Chia-Jui Hsu of Santa Clara CA (US)]][[Category:Chia-Jui Hsu of Santa Clara CA (US)]]

[[:Category:Mukund Sivaraman of Palo Alto CA (US)|Mukund Sivaraman of Palo Alto CA (US)]][[Category:Mukund Sivaraman of Palo Alto CA (US)]]

[[:Category:Vinod Kathail of Palo Alto CA (US)|Vinod Kathail of Palo Alto CA (US)]][[Category:Vinod Kathail of Palo Alto CA (US)]]

==MODELING AND COMPILING TENSOR PROCESSING APPLICATIONS FOR A COMPUTING PLATFORM USING MULTI-LAYER ADAPTIVE DATA FLOW GRAPHS - A simplified explanation of the abstract==


This abstract first appeared for US patent application 18091907 titled 'MODELING AND COMPILING TENSOR PROCESSING APPLICATIONS FOR A COMPUTING PLATFORM USING MULTI-LAYER ADAPTIVE DATA FLOW GRAPHS

'''Simplified Explanation''':
The patent application discusses modeling and compiling tensor processing applications using multi-layer adaptive data flow (ML-ADF) graphs, with a focus on optimizing resource sharing, kernel execution, memory reuse, and dataflow synchronization.

'''Key Features and Innovation''':
* Modeling tensor processing applications using ML-ADF graphs
* Folding ML-ADF graphs for temporal sharing of platform resources
* Computing schedules for runtime orchestration of kernel execution
* Memory reuse, tensor movement, and dataflow synchronization
* Generating binary code for target computing platforms and re-targetable controller code

'''Potential Applications''':
This technology can be applied in various fields such as artificial intelligence, machine learning, image processing, and data analytics.

'''Problems Solved''':
The technology addresses challenges related to efficient resource utilization, kernel execution optimization, and dataflow management in tensor processing applications.

'''Benefits''':
* Improved performance and efficiency in tensor processing applications
* Enhanced resource sharing and memory reuse
* Streamlined dataflow synchronization and kernel execution orchestration

'''Commercial Applications''':
Optimizing tensor processing applications for various industries such as healthcare, finance, autonomous vehicles, and robotics to improve efficiency and performance.

'''Prior Art''''':
Prior research in the field of tensor processing, dataflow optimization, and resource sharing in computing platforms can provide valuable insights into the development of this technology.

'''Frequently Updated Research''':
Stay updated on the latest advancements in tensor processing, dataflow optimization, and resource sharing techniques to enhance the efficiency and performance of this technology.

'''Questions about Tensor Processing Technology''':
1. How does the ML-ADF graph optimize resource sharing in tensor processing applications?
2. What are the key benefits of using multi-layer adaptive data flow graphs in kernel execution orchestration?


==Original Abstract Submitted==

Modeling and compiling tensor processing applications using multi-layer adaptive data flow (ML-ADF) graphs, including folding the ML-ADF graph for temporal sharing of platform resources, computing schedules for runtime orchestration of kernel execution, memory reuse, tensor and sub-volume movement, and dataflow synchronization, and generating binary code for processors of the target computing platform and re-targetable controller code. The ML-ADF graph may represent: tensor processing of a layer of a neural network as data flow through the data nodes and distribution to compute tiles across memory hierarchy; data flow amongst layers of the neural network using connections amongst data nodes of the respective layers; and multi-dimension data partitioning and distribution using tiling parameters associated with ports of the data nodes.



[[Category:G06F9/48]]
[[Category:G06F8/41]]
[[Category:G06F9/54]]
[[Category:CPC_G06F9/4881]]