在BrainScaleS-2移动系统上演示模拟推理
Demonstrating Analog Inference on the BrainScaleS-2 Mobile System
AI总结：
本文提出基于BrainScaleS-2 ASIC的紧凑型模拟推理引擎，在5.6W功耗下实现心电图分类，验证了其在边缘设备上的高效推理与未来在线学习的潜力。
摘要：
我们提出了BrainScaleS-2移动系统，这是一个基于BrainScaleS-2 ASIC的紧凑型模拟推理引擎，并展示了其在分类医疗心电图数据集方面的能力。该ASIC的模拟网络核心被用于执行卷积深度神经网络的乘加运算。在5.6W的系统功耗下，我们测得ASIC的总能耗为192uJ，并实现了每个心电图患者样本276us的分类时间。心房颤动患者被正确识别，检测率为(93.7±0.7)%，假阳性率为(14.0±1.0)%。由于其体积小、功耗范围和灵活的I/O能力，该系统可直接应用于边缘推理应用。它使得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将传统机器学习层与脉冲神经网络中的在线学习相结合。
英文摘要：
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 192uJ for the ASIC and achieve a classification time of 276us per electrocardiographic patient sample. Patients with atrial fibrillation are correctly identified with a detection rate of (93.7${\pm}$0.7)% at (14.0${\pm}$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.
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。
