Machine Learning Based Resource Utilization and Pre-estimation for Network on Chip (NoC) Communication

Network on chip (NoC) is the solution to solve the problem of larger system on chip and bus based communication system. NoC provides scalable, highly reliable and modular approach for on chip communication and related problems. The wireless communication technologies such as IEEE 802.15.4 Zigbee technology follow mesh, star and cluster tree topology. The paper focuses on the development of machine learning model for design and FPGA synthesis of mesh, ring and fat tree NoC for different cluster size (N = 2, 4, 8, 16, 32, 64, 128 and 256). The fat-tree based topologies incorporate more links near the root of the tree, in order to fulfill the requirement for higher communication demand closer to the root of the tree, as compared to its leafs. It is an indirect topology in which not all routers are identical in terms of number of ports connecting to other routers or elements in the network. The research article presents the use of machine learning techniques to predict the FPGA resource utilization for NoC in advance. The present study helps in NoC chip planning before designing the chip itself by taking into account known hardware design parameters, memory utilization and timing parameters such as minimum and maximum period, frequency support etc. The machine learning is carried out based on multiple linear regression, decision tree regression and random forest regression which estimate the accuracy of the design and good performance. The interprocess communication among nodes is verified using Virtex-5 FPGA, in which data flows in packets and can vary up to ‘n’ bit. The designs are developed in Xilinx ISE 14.2 and simulated in Modelsim 10.1b with the help of VHDL programming language. The developed model has been validated and has performed well on independent test data.