School of Integrated Circuits, Beijing National Research Center for Information Science and Technology (BNRist), Tsinghua University, 100084, Beijing, China.
Nat Commun. 2022 Apr 19;13(1):2026. doi: 10.1038/s41467-022-29712-8.
The human nervous system senses the physical world in an analogue but efficient way. As a crucial ability of the human brain, sound localization is a representative analogue computing task and often employed in virtual auditory systems. Different from well-demonstrated classification applications, all output neurons in localization tasks contribute to the predicted direction, introducing much higher challenges for hardware demonstration with memristor arrays. In this work, with the proposed multi-threshold-update scheme, we experimentally demonstrate the in-situ learning ability of the sound localization function in a 1K analogue memristor array. The experimental and evaluation results reveal that the scheme improves the training accuracy by ∼45.7% compared to the existing method and reduces the energy consumption by ∼184× relative to the previous work. This work represents a significant advance towards memristor-based auditory localization system with low energy consumption and high performance.
人类神经系统以模拟但高效的方式感知物理世界。作为人类大脑的一项关键能力，声源定位是一种具有代表性的模拟计算任务，常用于虚拟听觉系统。与表现良好的分类应用不同，定位任务中的所有输出神经元都有助于预测方向，这为使用忆阻器阵列进行硬件演示带来了更高的挑战。在这项工作中，我们提出了一种多阈值更新方案，通过实验在一个 1K 模拟忆阻器阵列中演示了声音定位功能的原位学习能力。实验和评估结果表明，与现有方法相比，该方案将训练精度提高了约 45.7%，与之前的工作相比，能量消耗降低了约 184 倍。这项工作朝着低能耗、高性能的基于忆阻器的听觉定位系统迈进了重要的一步。
