Design And Implementation For The Algorithm Of Microprocessor Hotspot Monitoring And Temperature Reconstruction

In recent years, the demand of electronic products is higher andhigher. Consumers not only pay attention to the performance of electronicproducts, but also concern about whether the product itself is light,compact, easy to carry. This has brought great challenges for the designof electronics products. As the core components of electronic products,microprocessor’s performance will have a direct impact on the userexperience of electronic products. Therefore, many chip manufacturersare promoting the integrated of microprocessor. It makes the feature sizeof integrated circuit decrease, power density and the chip temperatureincrease. Elevated chip temperature slows down the transistor speed,increases the leakage power consumption and decreases reliability. In thissituation, dynamic thermal management technique is widely used in thecurrent high-performance microprocessors to monitor the workingtemperature.Relying on the thermal sensor integrated in the microprocessor, dynamic thermal management technique using effective hotspotmonitoring algorithm and temperature reconstruction algorithm tocalculate the microprocessor temperature condition. According to thetemperature condition, it decide whether a warning or taking necessaryresponse. Therefore, hot spot monitoring algorithm and temperaturereconstruction algorithm directly affects the efficiency of dynamicthermal management. Based on the two kinds of application, this paperstudies algorithm and optimizes them. Then we use the experimentsimulation to verify the performance of these algorithms.In the hot spot monitoring part, this paper studies how to use a smallamount of heat sensors to monitor all hotspots with the K-meansclustering algorithm and the double cluster algorithms. Based on thesemethods, combined with characteristics of hotspot distribution, wepropose Thermal-Gradient-Aware K-means clustering algorithm andSingular-Point-Aware dual clustering algorithm to improve efficiency ofhot spot monitoring. The simulation results show that, these two kinds ofoptimization algorithms are better than the original algorithm in terms ofhotspot monitoring error and quantity of sensors. In the same errorrequirement, Singular-Point-Aware dual clustering algorithm uses lessnumber of sensors than Thermal-Gradient-Aware K-means clusteringalgorithm.In reconstruction of microprocessor temperature distribution part, this paper puts forward two ways of using sampled values of thenon-uniform distribution of temperature by thermal sensors to reconstructthe microprocessor temperature distribution. Among them, spectrumfiltering based on Voronoi using non-uniform sampling to uniformsampling transformation, reconstruct the temperature distribution simplyand rapidly by FFT and DCT. But the error is not impressive. Anothermethod is the inverse distance weighted average algorithm. The methodhas high precision, but costs much time. With the time requirement ofdynamic thermal management, we propose the inverse distance weightedalgorithm optimized by block and inverse distance weighted algorithmbased on Voronoi. It can greatly reduce the running time cost.