8位量化设计让无人机在低功耗下实时听声定位
SHIELD8-UAV: Sequential 8-bit Hardware Implementation of a Precision-Aware 1D-F-CNN for Low-Energy UAV Acoustic Detection and Temporal Tracking
- 逐层执行共享多精度数据通路，省去重复计算单元
- 特征维度压缩75%后仍保持89.91%识别准确率
- 适合资源受限的边缘设备，尤其对低功耗无人机有用
实时无人飞行器（UAV）边缘声学检测需在严苛的功耗与硬件限制下实现低延迟推理。本文提出SHIELD8-UAV，一种面向连续声学监测的精度感知一维特征驱动卷积神经网络（1D-F-CNN）加速器的串行8位硬件实现。该设计采用共享多精度数据通路进行逐层执行，避免了处理单元的重复配置。通过分层敏感量化框架支持FP32、BF16、INT8和FXP8模式，结合结构化通道剪枝将展平后的特征维度从35,072降至8,704（压缩75%），有效减少串行全连接层的计算周期。模型在FP32下达到89.91%检测准确率，8位模式下误差低于2.5%。加速器在Pynq-Z2 FPGA上仅消耗2,268 LUTs与0.94 W功率，端到端延迟为116毫秒，相比QuantMAC和LPRE分别降低37.8%与49.6%；逻辑资源使用比并行设计低5-9%。在UMC 40 nm工艺的ASIC综合中，最大工作频率达1.56 GHz，核心面积3.29 mm²，总功耗1.65 W。结果表明，串行执行结合精度感知量化与串行感知剪枝，可在无需大规模并行的情况下实现实用的低能边缘推理。
原文摘要 · Abstract (English)
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.
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