Inspired by biological processes, neuromorphic computing utilizes spiking neural networks (SNNs) to perform inference tasks, offering significant efficiency gains for workloads involving sequential data. Recent advances in hardware and software have demonstrated that embedding a few bits of payload in each spike exchanged between the spiking neurons can further enhance inference accuracy. In a split computing architecture, where the SNN is divided across two separate devices, the device storing the first layers must share information about the spikes generated by the local output neurons with the other device. Consequently, the advantages of multi-level spikes must be balanced against the challenges of transmitting additional bits between the two devices. This paper addresses these challenges by investigating a wireless neuromorphic split computing architecture employing multi-level SNNs. For this system, we present the design of digital and analog modulation schemes optimized for an orthogonal frequency division multiplexing (OFDM) radio interface. Simulation and experimental results using software-defined radios provide insights into the performance gains of multi-level SNN models and the optimal payload size as a function of the quality of the connection between a transmitter and receiver.

翻译：受生物过程启发，神经形态计算利用脉冲神经网络（SNNs）执行推理任务，为涉及序列数据的工作负载提供了显著的效率提升。硬件和软件的最新进展表明，在脉冲神经元之间交换的每个脉冲中嵌入少量有效载荷比特可以进一步提高推理精度。在拆分计算架构中，SNN被划分到两个独立的设备上，存储前几层的设备必须将本地输出神经元产生的脉冲信息与另一设备共享。因此，多级脉冲的优势必须与在两个设备间传输额外比特所带来的挑战相权衡。本文通过研究一种采用多级SNN的无线神经形态拆分计算架构来应对这些挑战。针对该系统，我们提出了专为正交频分复用（OFDM）无线电接口优化的数字与模拟调制方案设计。利用软件定义无线电进行的仿真和实验结果，揭示了多级SNN模型的性能增益以及最优有效载荷大小作为发射机与接收机之间连接质量函数的变化规律。