用忆阻器实现太空用神经网络加速，提升精度与可靠性。
Memristor-Based Neural Network Accelerators for Space Applications: Enhancing Performance with Temporal Averaging and SIRENs
- 通过比特切分与时间平均，缓解忆阻器器件偏差问题。
- 在小行星导航任务中误差率降至0.007，接近顶尖水平。
- 适合航天嵌入式AI系统，尤其关注能效与抗辐射场景。
忆阻器是一种新兴技术，可实现高能效和抗辐射的人工智能加速器，这对航天器上部署AI至关重要。然而，太空应用要求计算可靠且精确，而忆阻器件存在非理想性，如器件变异、电导漂移和故障。因此，将神经网络移植到忆阻器件常导致性能严重下降。本研究通过仿真表明，采用忆阻器的神经网络在航天任务（如小行星导航与测地）中可达到有竞争力的性能。结合比特切分、神经网络层的时间平均及周期性激活函数，使用RRAM器件将误差率从约0.07降至0.01，0.3降至0.007，分别接近当前最先进水平（0.003–0.005 和 0.003）。结果证明忆阻器在航天应用场景中的潜力，未来技术与神经网络改进将进一步缩小性能差距，充分释放其优势。
原文摘要 · Abstract (English)
Memristors are an emerging technology that enables artificial intelligence (AI) accelerators with high energy efficiency and radiation robustness -- properties that are vital for the deployment of AI on-board spacecraft. However, space applications require reliable and precise computations, while memristive devices suffer from non-idealities, such as device variability, conductance drifts, and device faults. Thus, porting neural networks (NNs) to memristive devices often faces the challenge of severe performance degradation. In this work, we show in simulations that memristor-based NNs achieve competitive performance levels on on-board tasks, such as navigation \& control and geodesy of asteroids. Through bit-slicing, temporal averaging of NN layers, and periodic activation functions, we improve initial results from around $0.07$ to $0.01$ and $0.3$ to $0.007$ for both tasks using RRAM devices, coming close to state-of-the-art levels ($0.003-0.005$ and $0.003$, respectively). Our results demonstrate the potential of memristors for on-board space applications, and we are convinced that future technology and NN improvements will further close the performance gap to fully unlock the benefits of memristors.
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