We present the BrainScaleS-2 mobile system as a compact analog inference engine based on the BrainScaleS-2 ASIC and demonstrate its capabilities at classifying a medical electrocardiogram dataset. The analog network core of the ASIC is utilized to perform the multiply-accumulate operations of a convolutional deep neural network. At a system power consumption of 5.6W, we measure a total energy consumption of $\mathrm {192 ~\mu \text {J} }$ for the ASIC and achieve a classification time of 276 $\mu$ s per electrocardiographic patient sample. Patients with atrial fibrillation are correctly identified with a detection rate of (93.7 ± 0.7)% at (14.0 ± 1.0)% false positives. The system is directly applicable to edge inference applications due to its small size, power envelope, and flexible I/O capabilities. It has enabled the BrainScaleS-2 ASIC to be operated reliably outside a specialized lab setting. In future applications, the system allows for a combination of conventional machine learning layers with online learning in spiking neural networks on a single neuromorphic platform.

中文翻译：

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在 BrainScaleS-2 移动系统上演示模拟推理

我们将 BrainScaleS-2 移动系统展示为基于 BrainScaleS-2 ASIC 的紧凑型模拟推理引擎，并展示其对医疗心电图数据集进行分类的能力。ASIC 的模拟网络核心用于执行卷积深度神经网络的乘法累加运算。在 5.6W 的系统功耗下，我们测量的总能耗为 $\mathrm {192 ~\mu \text {J} }$对于 ASIC 并实现 276 的分类时间 $\亩$ s 每个心电图患者样本。在 (14.0 ± 1.0)% 假阳性时，房颤患者的正确识别率为 (93.7 ± 0.7)%。由于其体积小、功率包络和灵活的 I/O 能力，该系统直接适用于边缘推理应用。它使 BrainScaleS-2 ASIC 能够在专业实验室环境之外可靠地运行。在未来的应用中，该系统允许在单个神经形态平台上的脉冲神经网络中将传统的机器学习层与在线学习相结合。