Research And Implementation Of Recommendation Algorithm Accellerator Based On Heterogeneous Computing Platform

In the current mobile Internet boom,any system that deals with users and wants to get a better user experience is inseparable from a complete recommendation system.Today's more common recommendation algorithms include content-based recommendation algorithms,collaborative filtering-based recommendations algorithm,association rules-based recommendation algorithm,etc.As the volume of system data grows,various recommendation algorithms take longer to process massive amounts of data from users.In order for the recommendation system to respond to the input data and process the recommendation results to the user more quickly,speeding up the execution speed of the recommendation algorithm has become an urgent problem to be solved.At present,for various existing recommendation algorithms,the cloud computing platform,the distributed general-purpose processor platform,and the graphics processor platform are mainly used for hardware acceleration.Although these methods improve the efficiency of the recommended algorithm to a certain extent,the accompanying problems of energy efficiency and cost can not be ignored.In this paper,a heterogeneous computing platform consisting of a general-purpose processor(CPU)and a field programmable gate array(FPGA)is used to study the hardware acceleration related problems of the recommended algorithm.Its main processor(CPU)can control the entire system while performing some simple data processing tasks;the coprocessor(FPGA)itself has very low power consumption,and has a large number of logic units that can be used to perform time-consuming Task.The two cooperate with each other to obtain a reduction in overall power consumption and an increase in efficiency when the algorithm is executed.The research work of this paper mainly includes the following aspects:1.Analyzing the current popular recommendation algorithm--the content-based recommendation algorithm,the collaborative filtering-based recommendation algorithm and the principle association rule-based recommendation algorithm.Aiming at the problems existing in the recommendation algorithm based on association rules,a new recommendation algorithm for hybrid nearest neighbors and association rules is proposed,and the recommended results are verified by experiments.the new algorithm is more suitable for the heterogeneous acceleration platform designed in this paper to support multiple recommendation algorithms.2.Researching the hotspots of several recommended algorithms,which summarizes four calculation hotspots,and their operation process is optimized in parallel and pipeline.3.Design several optimized computing hotspots into four hardware-accelerated IP cores,complete the design of their internal structure,IO interface and key modules,and propose a targeted HLS optimization strategy.At the same time,the design of the device driver layer and user interface layer of the entire hardware acceleration system is completed.4.The ZYNQ series experimental board was selected as the heterogeneous computing platform to realize the prototype of the whole hardware accelerator.By comparing the experiments of several similar recommended tasks on the heterogeneous computing platform and several general-purpose processor(CPU)platforms,the verification was verified.Calculating the parallelization of the hotspot and the acceleration effect of the pipeline optimization,the expected conclusion that the heterogeneous computing platform has good acceleration effect on the basis of high energy efficiency compared with the three general processor platforms is obtained.