Dell products l.p. (20240095339). METHOD, DEVICE, AND COMPUTER PROGRAM PRODUCT FOR EXECUTING COMPUTER PROGRAMS simplified abstract
Contents
- 1 METHOD, DEVICE, AND COMPUTER PROGRAM PRODUCT FOR EXECUTING COMPUTER PROGRAMS
- 1.1 Organization Name
- 1.2 Inventor(s)
- 1.3 METHOD, DEVICE, AND COMPUTER PROGRAM PRODUCT FOR EXECUTING COMPUTER PROGRAMS - 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
METHOD, DEVICE, AND COMPUTER PROGRAM PRODUCT FOR EXECUTING COMPUTER PROGRAMS
Organization Name
Inventor(s)
Tianxiang Chen of Shanghai (CN)
METHOD, DEVICE, AND COMPUTER PROGRAM PRODUCT FOR EXECUTING COMPUTER PROGRAMS - A simplified explanation of the abstract
This abstract first appeared for US patent application 20240095339 titled 'METHOD, DEVICE, AND COMPUTER PROGRAM PRODUCT FOR EXECUTING COMPUTER PROGRAMS
Simplified Explanation
The abstract of the patent application describes a method, device, and computer program product for executing computer programs using a deep neural network with a hooking portion outside a trusted execution environment (TEE).
- The method involves implementing multiple executions of a deep neural network with a hooking portion outside a TEE.
- An operator in the hooking portion invokes a corresponding execution operator in the TEE when executed outside the TEE.
- The method includes determining a computation graph corresponding to the hooking portion based on the invocation of the corresponding execution operator in the TEE.
- The execution operator corresponding to the hooking portion is executed in the TEE during the execution of the deep neural network after the multiple executions.
Potential Applications
This technology could be applied in secure computing environments, such as in data centers or cloud computing, where ensuring the integrity of the execution of computer programs is crucial.
Problems Solved
This technology addresses the issue of executing computer programs securely by utilizing a trusted execution environment to verify the execution of deep neural networks with hooking portions.
Benefits
The benefits of this technology include enhanced security in executing computer programs, particularly those involving deep neural networks, by leveraging a trusted execution environment to ensure the integrity of the execution process.
Potential Commercial Applications
One potential commercial application of this technology could be in the field of cybersecurity, where secure execution of computer programs is essential to protect sensitive data and prevent unauthorized access.
Possible Prior Art
One possible prior art for this technology could be research or patents related to secure execution environments for computer programs, particularly in the context of deep learning and neural networks.
Unanswered Questions
How does this technology compare to existing methods of securing the execution of computer programs?
This article does not provide a direct comparison to existing methods of securing the execution of computer programs. It would be beneficial to understand the specific advantages and limitations of this technology compared to other approaches in the field.
What are the potential limitations or challenges in implementing this technology in real-world applications?
The article does not address the potential limitations or challenges in implementing this technology in practical settings. It would be important to explore factors such as scalability, performance impact, and compatibility with existing systems to assess the feasibility of widespread adoption.
Original Abstract Submitted
embodiments of the present disclosure relate to a method, a device, and a computer program product for executing computer programs. the method includes implementing multiple executions of a deep neural network that includes a hooking portion outside a trusted execution environment (tee), wherein an operator in the hooking portion, when executed outside the tee, invokes a corresponding execution operator in the tee. during the period, the method includes determining, on the basis of the invocation of the corresponding execution operator in the tee by the operator in the hooking portion, a computation graph corresponding to the hooking portion. the method further includes executing, on the basis of the computation graph, the execution operator corresponding to the hooking portion in the tee during the execution of the deep neural network after the multiple executions.
