Real-time unmanned aerial vehicle (UAV) acoustic detection at the edge demands low-latency inference under strict power and hardware limits. This paper presents SHIELD8-UAV, a sequential 8-bit hardware implementation of a precision-aware 1D feature-driven CNN (1D-F-CNN) accelerator for continuous acoustic monitoring. The design performs layer-wise execution on a shared multi-precision datapath, eliminating the need for replicated processing elements. A layer-sensitivity quantisation framework supports FP32, BF16, INT8, and FXP8 modes, while structured channel pruning reduces the flattened feature dimension from 35,072 to 8,704 (75%), thereby lowering serialised dense-layer cycles. The model achieves 89.91% detection accuracy in FP32 with less than 2.5% degradation in 8-bit modes. The accelerator uses 2,268 LUTs and 0.94 W power with 116 ms end-to-end latency, achieving 37.8% and 49.6% latency reduction compared with QuantMAC and LPRE, respectively, on a Pynq-Z2 FPGA, and 5-9% lower logic usage than parallel designs. ASIC synthesis in UMC 40 nm technology shows a maximum operating frequency of 1.56 GHz, 3.29 mm2 core area, and 1.65 W total power. These results demonstrate that sequential execution combined with precision-aware quantisation and serialisation-aware pruning enables practical low-energy edge inference without relying on massive parallelism.

翻译：在边缘设备上进行实时无人机声学检测，要求在严格的功耗与硬件限制下实现低延迟推理。本文提出了SHIELD8-UAV，一种用于连续声学监测的精度感知一维特征驱动CNN加速器的序列化8位硬件实现。该设计在共享的多精度数据通路上执行逐层运算，无需复制处理单元。层敏感量化框架支持FP32、BF16、INT8和FXP8模式，同时结构化通道剪枝将展平特征维度从35,072减少到8,704（75%），从而降低了序列化全连接层的计算周期。该模型在FP32模式下实现了89.91%的检测准确率，在8位模式下准确率下降小于2.5%。该加速器使用2,268个LUT，功耗为0.94 W，端到端延迟为116 ms。在Pynq-Z2 FPGA上，与QuantMAC和LPRE相比，延迟分别降低了37.8%和49.6%，且逻辑资源使用量比并行设计低5-9%。采用UMC 40 nm工艺的ASIC综合结果显示，其最高工作频率为1.56 GHz，核心面积为3.29 mm²，总功耗为1.65 W。这些结果表明，序列化执行结合精度感知量化与序列化感知剪枝，能够在不依赖大规模并行性的情况下实现实用的低功耗边缘推理。