Evaluation of APCMA and CSMA/CA in Wireless Brain-Inspired Computing

Wireless Brain-Inspired Computing (WiBIC), which implements spiking neural networks (SNNs) in a distributed manner on IoT devices, is gaining attention as a technology enabling serverless, autonomous learning. Since SNN learning relies on precise spike timing information, selecting the wireless communication protocol for transmitting spike signals is critically important. However, protocols like Carrier Sense Multiple Access with Collision Avoidance (CSMA/CA), widely used in IoT, introduce probabilistic delays to avoid collisions, potentially degrading SNN learning performance. This research proposes Asynchronous Pulse Code Multiple Access (APCMA) as a communication method suitable for spike transmission in WiBIC. In actual device experiments, CSMA/CA caused significant delay jitter and packet loss due to communication contention, whereas APCMA maintained nearly constant delay with no observed loss. For XOR learning, the learning success rate using CSMA/CA decreased as communication density increased, while APCMA maintained stable high success rate regardless of communication density.