On the Effectiveness of Quantization and Pruning on the Performance of FPGAs-based NN Temperature Estimation

Abstract

A well-functioning thermal management system on the chip requires knowledge of the current temperature and the potential changes in temperature in the near future. This information is important for ensuring proactive thermal management on the chip. However, the limited number of sensors on the chip makes it difficult to accomplish this task. Hence we proposed a neural network based approach to predict the temperature map of the chip. To solve the problem, we have implemented two different neural networks, one is a feedforward network and the other uses recurrent neural networks. Our proposed method requires only performance counters measure to predict the temperature map of the chip during the runtime. Each of the two models shows promising results regarding the estimation of the temperature map on the chip. The recurrent neural network outperformed the feedforward neural network. Furthermore, both networks have been quantized, pruned, and the feedforward network has been compiled into FPGA logic. Therefore, the network could be embedded in the chip, whether it be an ASIC or an FPGA.