用忆阻器加速航天器导航控制神经网络，提升能效并抗辐射。
Guidance and Control Neural Network Acceleration using Memristors
- 用相变/阻变忆阻器实现神经网络在内存中计算，降低功耗。
- 忆阻器加速的网络可学习专家动作，噪声影响精度但可修复。
- 适合小型卫星、深空探测等严苛空间环境中的AI部署。
近年来，航天领域正探索将人工神经网络（ANN）用于各类星载应用。然而，小型卫星和立方星受限于能源预算，且现代芯片面临辐射威胁，制约了AI应用发展。为此，亟需开发满足计算需求、同时适应低功耗与抗辐射要求的神经网络加速器。本文研究利用相变存储器（PCM）和阻性随机存取存储器（RRAM）忆阻器，在轨实现内存计算型AI加速，针对航天场景中的引导与控制神经网络（G&CNET）进行仿真测试。实验涵盖多种工况及两类器件，考虑了噪声、电导漂移等非理想因素。结果表明，忆阻器加速系统能有效学习专家行为，尽管噪声会降低精度；通过退化后重训练，性能可恢复至初始水平。本研究为未来基于忆阻器的太空AI加速器奠定了基础，凸显其潜力与进一步研究的必要性。
原文摘要 · Abstract (English)
In recent years, the space community has been exploring the possibilities of Artificial Intelligence (AI), specifically Artificial Neural Networks (ANNs), for a variety of on board applications. However, this development is limited by the restricted energy budget of smallsats and cubesats as well as radiation concerns plaguing modern chips. This necessitates research into neural network accelerators capable of meeting these requirements whilst satisfying the compute and performance needs of the application. This paper explores the use of Phase-Change Memory (PCM) and Resistive Random-Access Memory (RRAM) memristors for on-board in-memory computing AI acceleration in space applications. A guidance and control neural network (G\&CNET) accelerated using memristors is simulated in a variety of scenarios and with both device types to evaluate the performance of memristor-based accelerators, considering device non-idealities such as noise and conductance drift. We show that the memristive accelerator is able to learn the expert actions, though challenges remain with the impact of noise on accuracy. We also show that re-training after degradation is able to restore performance to nominal levels. This study provides a foundation for future research into memristor-based AI accelerators for space, highlighting their potential and the need for further investigation.
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