Reconfigurable computing device, processor and method

Abstract

The invention relates to a reconfigurable computing device, a processor and a method, which are used for federated learning and privacy computing. The reconfigurable computing device comprises: a static reconfigurable part which is used for receiving to-be-processed data and an operation scene and is configured to perform function reconstruction according to the operation scene before the reconfigurable computing device is powered on; and the dynamic reconfigurable part that is used for executing the calculation of the data to be processed in the operation scene and is configured to perform function reconstruction in the calculation of the reconfigurable calculation device. The computing resources of the dynamic reconfigurable part comprise a first part of computing resources used for modular exponentiation computing and a second part of computing resources used for modular multiplication computing, and the proportion of the first part of computing resources relative to the second part of computing resources is determined according to the bit width of the data to be processed. In this way, the resource utilization rate is increased while high computing performance is kept.

Description

technical field [0001] The present application relates to the technical field of data security and privacy protection, and specifically relates to a reconfigurable computing device, processor and method, which are applied to federated learning and privacy computing. Background technique [0002] With the development of application fields such as artificial intelligence and big data mining analysis, the demand for data volume is increasing. For example, training artificial intelligence application models requires a large amount of training data with appropriate data labels or feature values. High-quality data often come from the application data generated and accumulated in business activities. However, application data is often scattered in the hands of different organizations and individuals. For example, transaction data is scattered in various financial institutions, and medical diagnosis data is scattered in various medical institutions. Cross-industry and cross-field ...

AI Technical Summary

Problems solved by technology

[0004] In order to solve the problem of being able to cope with complex and changeable requirements in specific applications and to reduce energy consumption by maximizing the use of computing resources and storage resources while maintaining high computing performance, the embodiments of the present application provide a federated A reconfigurable computing device, processor, and method for learning and privacy computing, thereby realizing the adaptability and flexibility to adapt to changes in data bit width, and helping to improve computing speed and resource utilization

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