Sensory processing with neuromorphic systems is typically done by using either event-based sensors or translating input signals to spikes before presenting them to the neuromorphic processor. Here, we offer an alternative approach: direct analog signal injection eliminates superfluous and power-intensive analog-to-digital and digital-to-analog conversions, making it particularly suitable for efficient near-sensor processing. We demonstrate this by using the accelerated BrainScaleS-2 mixed-signal neuromorphic research platform and interfacing it directly to microphones and a servo-motor-driven actuator. Utilizing BrainScaleS-2's 1000-fold acceleration factor, we employ a spiking neural network to transform interaural time differences into a spatial code and thereby predict the location of sound sources. Our primary contributions are the first demonstrations of direct, continuous-valued sensor data injection into the analog compute units of the BrainScaleS-2 ASIC, and actuator control using its embedded microprocessors. This enables a fully on-chip processing pipeline$\unicode{x2014}$from sensory input handling, via spiking neural network processing to physical action. We showcase this by programming the system to localize and align a servo motor with the spatial direction of transient noise peaks in real-time.

翻译：神经形态系统的感觉处理通常采用两种方式：使用事件驱动传感器，或将输入信号转换为脉冲序列后再馈入神经形态处理器。本文提出一种替代方案：直接模拟信号注入技术，该方法消除了冗余且高功耗的模数/数模转换环节，特别适用于高效的近传感器处理场景。我们通过加速型BrainScaleS-2混合信号神经形态研究平台，将其直接与麦克风阵列及伺服电机驱动的执行器对接，验证了该方案的可行性。利用BrainScaleS-2平台1000倍的加速特性，我们构建了脉冲神经网络将双耳时间差转换为空间编码，进而实现声源定位。本研究的核心贡献在于：首次实现了连续值传感器数据直接注入BrainScaleS-2专用集成电路的模拟计算单元，并利用其嵌入式微处理器完成执行器控制。由此构建了从传感器输入处理、经脉冲神经网络运算到物理动作执行的全片上处理流程$\unicode{x2014}$我们通过编程使系统能够实时定位瞬态噪声峰值空间方向并控制伺服电机实现方位对准，展示了该技术的实际应用效能。