An energy-efficient hardware module for edge detection using XNOR-Popcount in resource-constrained devices - Van-Khoa Pham; Lai Le (Institute of Advanced Engineering and Science, 2026)
Edge detection is a fundamental building block in many embedded vision tasks, including drone navigation, IoT cameras, and wearable devices. However, traditional edge detectors based on multiply–accumulate (MAC) operations are poorly suited to the tight power and area budgets of such resource-constrained hardware. This work introduces a fully synthesizable Prewitt edge detector that replaces MAC operations with 1-bit XNOR– Popcount logic. Incoming 8-bit pixels and ±1 kernel coefficients are binarized, processed by parallel XNOR gates, and tallied by a lightweight Popcount adder tree, eliminating all multipliers and DSP slices. Prototyped on a Xilinx Zynq-7020 FPGA, the proposed design reduces lookup-table usage by 55% and flip-flop count by 26%, cuts dynamic power by about 60%, and supports clock frequencies up to five times higher than a MACbased core. Frame-level evaluations on the MNIST and ORL datasets show near-lossless edge fidelity, with per-image dissimilarity scores below 0.08 and throughput gains approaching four times. These results demonstrate that hardware-aware binary approximations can enable real-time, energyefficient edge detection for embedded AI systems without sacrificing functional accuracy.