Q-A²NN: Quantized All-Adder Neural Networks for Onboard Remote Sensing Scene Classification

Performing remote sensing scene classification (RSSC) directly on satellites can alleviate data downlink burdens and reduce latency. Compared to convolutional neural networks (CNNs), the all-adder neural network (A²NN) is a novel basic neural network that is more suitable for onboard RSSC, enabling lower computational overhead by eliminating multiplication operations in convolutional layers. However, the extensive floating-point data and operations in A²NNs still lead to significant storage overhead and power consumption during hardware deployment. In this article, a shared scaling factor-based de-biasing quantization (SSDQ) method tailored for the quantization of A²NNs is proposed to address this issue, including a powers-of-two (POT)-based shared scaling factor quantization scheme and a multi-dimensional de-biasing (MDD) quantization strategy. Specifically, the POT-based shared scaling factor quantization scheme converts the adder filters in A²NNs to quantized adder filters with hardware-friendly integer input activations, weights, and operations. Thus, quantized A²NNs (Q-A²NNs) composed of quantized adder filters have lower computational and memory overheads than A²NNs, increasing their utility in hardware deployment. Although low-bit-width Q-A²NNs exhibit significantly reduced RSSC accuracy compared to A²NNs, this issue can be alleviated by employing the proposed MDD quantization strategy, which combines a weight-debiasing (WD) strategy, which reduces performance degradation due to deviations in the quantized weights, with a feature-debiasing (FD) strategy, which enhances the classification performance of Q-A²NNs through minimizing deviations among the output features of each layer. Extensive experiments and analyses demonstrate that the proposed SSDQ method can efficiently quantize A²NNs to obtain Q-A²NNs with low computational and memory overheads while maintaining comparable performance to A²NNs, thus having high potential for onboard RSSC.