面向神经形态硬件上个体化制动意图检测的少样本迁移学习
Few-Shot Transfer Learning for Individualized Braking Intent Detection on Neuromorphic Hardware
AI总结：
本文提出在 Akida AKD1000 上用少样本迁移学习将群体级 CSNN 快速适配为个体化脑电图制动意图模型，3 个 epoch 即达 90% 以上性能且显著节能。
摘要：
目的：本工作探索使用少样本迁移学习方法，在 BrainChip Akida AKD1000 神经形态片上系统上训练并实现卷积脉冲神经网络（CSNN），以便利用脑电图数据开发个体层面模型，而非传统采用的群体层面模型。主要结果：展示了上述方法在开发个体特异性制动意图预测模型方面的有效性：该方法可在仅三个训练 epoch 内快速适配群体层面模型，同时实现至少 90% 的准确率、真阳性率和真阴性率。此外，结果表明，与 Intel Xeon 中央处理器相比，使用 Akida AKD1000 处理器进行网络推理时，功耗降低超过 97%，而延迟仅增加 1.3 倍，体现了神经形态硬件的能效。在随后使用 19 个通道中的 5 个通道子集开展的消融研究中，也获得了相似结果。
英文摘要：
Objective: This work explores use of a few-shot transfer learning method to train and implement a convolutional spiking neural network (CSNN) on a BrainChip Akida AKD1000 neuromorphic system-on-chip for developing individual-level, instead of traditionally used group-level, models using electroencephalographic data. Main Results: Efficacy of the above methodology to develop individual-specific braking intention predictive models by rapidly adapting the group-level model in as few as three training epochs while achieving at least 90% accuracy, true positive rate and true negative rate is presented. Further, results show the energy-efficiency of the neuromorphic hardware through a power reduction of over 97% with only a $1.3* increase in latency when using the Akida AKD1000 processor for network inference compared to an Intel Xeon central processing unit. Similar results were obtained in a subsequent ablation study using a subset of five out of 19 channels.
机构、总结、中文摘要均由AI分析而来，可能存在错误，请以论文正文为准。
