VLSI Implementation of Neural Network Driven Augmented FSM

Abstract

This thesis reports the VLSI implementation of an NN (Neural Network) based emergent behavior model for high-speed robot control. Augmented FSM (Finite- State Machine) is considered to implement the emergent behavior. We performed a system level simulation using our proposed model. For system level simulation, we have used Python base TensorFlow model to implement the Neural Network. Then, we transformed the model to RTL (Register Transfer Level) for circuit simulation. For RTL modeling we have used Verilog (Xilinx, Quartus Prime and iVerilog) and for simulation we have used (Modelsim and GTK wave). In this study, we considered multiple inputs and multiple-outputs NN. Our implementation method improves the speed of execution and accuracy and compares the result with the conventional neural network. For activation function in NN, we implemented sigmoid function with second-order approximation to reduce complexity. We used the walking gesture of the Kondo KHR 3HV robot to verify the model. Finally, we design NN based augmented-AI chip for high-speed robotics applications.