Boe Technology Group Co., Ltd.

Beijing University of Technology

Chengdu Boe Optoelectronics Technology Co., Ltd.

Hefei Xinsheng Optoelectronics Technology Co., Ltd.

Juan Fang

Huijing Yang

Ziyi Teng

Yao Huang

Zhenfei Cai

Min Chen

Xuan Wang

Juntao Chen

Xiaomei Wei

Shuaibing Lu

Min Cai

The invention relates to an optimization method for mobile edge cache based on federated learning. The situation that the user mobility and the content popularity change continuously in the range of a single base station is considered, and the cache hit rate is increased by predicting the content popularity and placing the request content in an edge cache in advance. The method includes obtaining a user moment trajectory table to simulate a moving path of a user by using an RWP random path point model, selecting the user participating in FL local training in a clustering, and threshold value combination mode in consideration of local training consumption, performing global model aggregation by using an attention mechanism to control model weight, and performing global prediction according to an obtained global prediction model. The predicted request content is cached to the server in advance.

Method for federated-learning-based mobile edge cache optimization

A shift register includes a first control sub-circuit, a second control sub-circuit, a pull-up control sub-circuit and an output control sub-circuit, wherein the first control sub-circuit is configured to provide a signal of a third power supply terminal or a clock signal terminal to a first node and a third node under the control of a signal input terminal, the clock signal terminal and a second node; the pull-up control sub-circuit is configured to provide a signal of a second power supply terminal to the first node under the control of the third node; the second control sub-circuit is configured to provide a signal of the signal input terminal to the second node and a fourth node under the control of the clock signal terminal and the first power supply terminal.

Shift register, driving method thereof, display substrate and display device

The invention discloses a prefetch-adaptive intelligent cache replacement policy for high performance, in the presence of hardware prefetching, a prefetch request and a demand request are distinguished, a prefetch predictor based on an ISVM (Integer Support Vector Machine) is used for carrying out re-reference interval prediction on a cache line of prefetching access loading, and a demand predictor based on an ISVM is utilized to carry out re-reference interval prediction on a cache line of demand access loading. A PC of a current access load instruction and PCs of past load instructions in an access historical record are input, different ISVM predictors are designed for prefetch and demand requests, reuse prediction is performed on a loaded cache line by taking a request type as granularity, the accuracy of cache line reuse prediction in the presence of prefetching is improved, and performance benefits from hardware prefetching and cache replacement is better fused.

Prefetch-adaptive intelligent cache replacement policy for high performance

An array substrate is disclosed, which includes a connection structure of a second short-circuit ring and one corresponding data line, and this connection structure includes: a first electrode disposed on a base substrate; a connection line disposed on the first electrode; a first insulating layer disposed on the first electrode and the connection line, in which the data line connected with the second short-circuit ring is disposed on the first insulating layer; a second insulating layer disposed on the data line connected with the second short-circuit ring; and a second electrode disposed on the second insulating layer, in which the second electrode is connected with the data line connected with the second short-circuit ring through a first via hole and connected with the first electrode through a second via hole.

Array substrate, display panel and method for manufacturing array substrate

Growing community of inventors

Beijing, China

Juan Fang