A Kernel-Aware Framework for Energy-Efficient FPGA Edge Detection Using XNOR-popcount and Selective Approximate Arithmetic
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Abstract
Edge-vision systems on resource-constrained platforms require low-latency and energy-efficient front-end processing. Conventional gradient-based edge operators continue to rely on multiply-accumulate (MAC) operations, which can increase logic utilization, switching activity, and power consumption in eld-programmable gate array (FPGA) implementations. This study introduces a kernel-aware arithmetic-selection framework, supported by synthesized evidence, for energy-efficient FPGA-based edge detection. The framework combines XNORpopcount-based arithmetic mapping with selective approximate computation. Its central design principle is to replace MAC operations with exclusive-NOR-population count (XNORpopcount) when the kernel structure is suitable for binary-friendly arithmetic, and to apply approximation only to the remaining adder-dominated stages when a complete XNOR-popcount mapping is not practical. Under this rule, Prewitt-like operators are mapped to an XNORpopcount datapath over their active nonzero taps. In contrast, Sobel-like operators are realized using a hybrid datapath that combines binary matching, shift-add processing, and approximate accumulation. The resulting framework shows that multiplier removal is the dominant source of hardware savings, while approximate arithmetic provides a controlled secondary optimization. Overall, the proposed approach establishes a structured design methodology for low-power FPGA edge-detection architectures on embedded platforms.
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