SHIELD8-UAV: Sequential 8-bit Hardware Implementation of a Precision-Aware 1D-F-CNN for Low-Energy UAV Acoustic Detection and Temporal Tracking

TL;DR

SHIELD8-UAV introduces a low-energy, sequential 8-bit CNN hardware accelerator for real-time UAV acoustic detection, achieving high accuracy and significant latency reduction on FPGA and ASIC platforms.

Contribution

The paper presents a novel sequential 8-bit CNN accelerator with a layer-sensitivity quantisation framework and structured pruning, optimized for low-power UAV edge applications.

Findings

Achieves 89.91% detection accuracy with less than 2.5% degradation in 8-bit modes.

Reduces latency by up to 49.6% compared to existing accelerators.

Demonstrates efficient FPGA and ASIC implementations with low power and high frequency.

Abstract

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.