Implementation of Spiking Neural Network with Wireless Communications

Energy-Efficient Distributed Spiking Neural Network for Wireless Edge Intelligence

“…Recently there is a growing interest in applying SNNs for edge applications [2], [12], [16]- [22], [24]- [26]. In [16] and [17], distributed wireless SNN has been implemented on field programmable gate array (FPGA) for an exclusive or (XOR) computation task, where carrier sense multiple access/collision detection (CSMA/CD) and time division multiple access (TDMA) have been used for spikes transmission, respectively. By analysing the performance of distributed SNN in terms of inference accuracy and neural activity under spike losses [12], the resilience of SNNs in wireless environments has been demonstrated.…”

“…Nevertheless, the area of distributed SNNs is still under investigation, especially in terms of how to efficiently deploy distributed SNNs in resource constrained edge scenarios. More precisely, the systems in [16] and [17] only consider a simple XOR computation tasks with two input neurons, which may not be generalizable to real-world scenarios. To the best of the authors' knowledge, there are still several issues to be considered, such as practical communication protocols on the collaboration of multiple edge devices, quantitative analysis of the spike capacity and energy consumptions, and efficient algorithms for allocating the limited bandwidth resources and minimizing the system power consumptions.…”

“…We show that the system exists stationary states where the percentage of firing neurons is constant. An alternating optimization based algorithm is then developed for problem (16).…”

“…Problem ( 20) is significantly simplified compared with problem (16). However, optimization variables in Ω are still coupled and the objective function (20a) is non-convex.…”