Placement and routing for 3D-FPGAs using reinforcement learning and support vector machines

The primary advantage of using 3D-FPGA over 2D-FPGA is that the vertical stacking of active layers reduce the Manhattan distance between the components in 3D-FPGA than when placed on 2D-FPGA. This results in a considerable reduction in total interconnect length. Reduced wire length eventually leads to reduction in delay and hence improved performance and speed. Design of an efficient placement and routing algorithm for 3D-FPGA that fully exploits the above mentioned advantage is a problem of deep research and commercial interest. In this paper, an efficient placement and routing algorithm is proposed for 3D-FPGAs which yields better results in terms of total interconnect length and channel-width. The proposed algorithm employs two important techniques, namely, reinforcement learning (RL) and support vector machines (SVMs), to perform the placement. The proposed algorithm is implemented and tested on standard benchmark circuits and the results obtained are encouraging. This is one of the very few instances where reinforcement learning is used for solving a problem in the area of VLSI.