18519689. CUSTOM COMPUTE CORES IN INTEGRATED CIRCUIT DEVICES simplified abstract (Micron Technology, Inc.)
Contents
- 1 CUSTOM COMPUTE CORES IN INTEGRATED CIRCUIT DEVICES
- 1.1 Organization Name
- 1.2 Inventor(s)
- 1.3 CUSTOM COMPUTE CORES IN INTEGRATED CIRCUIT DEVICES - 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
CUSTOM COMPUTE CORES IN INTEGRATED CIRCUIT DEVICES
Organization Name
Inventor(s)
Gavin L. Huggins of Meridian ID (US)
CUSTOM COMPUTE CORES IN INTEGRATED CIRCUIT DEVICES - A simplified explanation of the abstract
This abstract first appeared for US patent application 18519689 titled 'CUSTOM COMPUTE CORES IN INTEGRATED CIRCUIT DEVICES
Simplified Explanation
The system described in the patent application includes a processor and a hardware accelerator that works in conjunction with the processor. The hardware accelerator consists of data analysis elements that analyze a data stream based on configuration data and output a result. Additionally, the hardware accelerator includes an integrated circuit device with a DMA engine that handles the input and output data for the data analysis elements, preprocessing cores that perform custom preprocessing functions on the input data before it is sent to the data analysis elements, and post-processing cores that perform custom post-processing functions on the output data before it is sent to the processor.
- The system includes a processor and a hardware accelerator.
- The hardware accelerator has data analysis elements that analyze data streams and output results.
- An integrated circuit device with a DMA engine manages input and output data for the data analysis elements.
- Preprocessing cores perform custom preprocessing functions on input data.
- Post-processing cores perform custom post-processing functions on output data.
Potential Applications
This technology could be applied in various fields such as data analysis, real-time processing, and high-performance computing.
Problems Solved
This technology solves the problem of efficiently analyzing large data streams in real-time and offloading processing tasks from the main processor.
Benefits
The benefits of this technology include improved data analysis speed, reduced workload on the main processor, and the ability to perform custom preprocessing and post-processing functions on data streams.
Potential Commercial Applications
One potential commercial application of this technology could be in the development of high-speed data processing systems for industries such as finance, telecommunications, and scientific research.
Possible Prior Art
One possible prior art for this technology could be similar hardware accelerators used in the field of data processing and analysis.
Unanswered Questions
How does this technology compare to existing hardware accelerators in terms of performance and efficiency?
This article does not provide a direct comparison between this technology and existing hardware accelerators in terms of performance and efficiency. Further research and testing would be needed to determine the specific advantages of this technology over existing solutions.
What are the potential limitations or drawbacks of implementing this technology in a system?
This article does not address any potential limitations or drawbacks of implementing this technology in a system. It would be important to consider factors such as cost, compatibility, and scalability when evaluating the feasibility of integrating this technology into a system.
Original Abstract Submitted
A system includes a processor and a hardware accelerator coupled to the processor. The hardware accelerator includes data analysis elements configured to analyze a data stream based on configuration data and to output a result, and an integrated circuit device that includes a DMA engine that writes input data to and read output data from the data analysis elements, one or more preprocessing cores that receive the input data from the DMA engine prior to the DMA engine writing the input data to the one or more data analysis elements and perform custom preprocessing functions on the input data, and one or more post-processing cores that receive the output data from the DMA engine after the output data is read from the data analysis elements but prior to the output data being output to the processor and perform custom post-processing functions on the output data.
