Deploying full-precision multilayer perceptron (MLP) controllers for DC–DC converters on low-cost edge FPGAs is constrained by look-up table (LUT) and digital signal processor (DSP) budgets, particularly when fully-parallel datapaths are required at the converter switching rate. We present a ternary-weight MLP for buck converter duty-cycle control in which every weight is constrained to {-1, 0, +1}, mapping the forward pass to wires, inverters, and pruned connections at synthesis time. The network is trained by behavioral cloning from a Tustin-discretized lag compensator and realized as Q15.16 synthesizable Verilog on a Xilinx Zynq-7020, with closed-loop verification through System Generator co-simulation. The ternary core uses 2,830 LUTs (5.3%) and zero DSPs, versus 84,769 LUTs (159%) and 70 DSPs for an architecturally matched Q15.16 baseline that does not fit on the device. These results indicate that ternary weight quantization can enable fully-parallel neural converter control on low-cost edge FPGAs.
 

FPGA,neural network hardware,ternary weight quantization,DC–DC converters,buck converter control,behavioral cloning