Reduction of Processing Time for Wireless Spiking Neural Network Using Wireless Communication Devices for IoT

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.…”

“…Specifically, at time t, the potential of the fired neurons will be reset to zero, which produces a Dirac impulse at the membrane potential density function p(U = 0)[t]. The membrane potentials of these fired neurons then evolve according to (17). Hence, we can divide the neurons based on their firing ages k. Denoting the percentage of neurons that fires at time t − k and did not fire until t as η k and their corresponding membrane potential as Φ k .…”

“…Hence, we can divide the neurons based on their firing ages k. Denoting the percentage of neurons that fires at time t − k and did not fire until t as η k and their corresponding membrane potential as Φ k . The percentage of firing neurons can be written as (17), the relationship between these neuron dynamics can be written as…”