Resilience Analysis of Distributed Wireless Spiking Neural Networks

Abstract

Spiking neural networks (SNN) are expected to enable several use-cases in future communication networks (beyond 5G and 6G), as edge AI and battery-constrained systems can leverage the fast computation and high-power efficiency offered by SNNs. In this work we consider a Distributed Wireless SNN (DW-SNN) system and we analyze its performance in terms of inference accuracy and total neural activity when radio losses are applied to spikes transferred during the inference phase. Our aim is to understand how radio losses impact performance when considering different SNN spike communication types, i.e., input, excitatory, and inhibitory spikes. Then we evaluate the impact of different traffic prioritization approaches among SNN spikes when considering a shared channel capacity being available for SNN activity. From these analyses, we derive some key insights and features that can be considered when applying a DW-SNN and handling its traffic over wireless communication systems. Finally, we report a prototype implementation of DW-SNN using custom-built IoT components, which we use to further investigate different coverage scenarios.

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.…”

“…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. The novel neuromorphic semantic communication paradigm in [18] combines the event-driven sensing, spiked-based computation, and impulse radio (IR) for remote inference.…”

“…Numerous specialized hardware has been designed to emulate SNNs, such as IBM's TrueNorth [5], Intel's Loihi [6], Tianjic [7], and Neurogrid [8]. Moreover, SNNs naturally integrate with neuromorphic sensors that directly generate spike-type data [9]- [11], enabling SNNs to learn and infer in an end-to-end bio-plausible manner with ultra low power consumption Recently, the distributed SNN has been introduced [12] for the following reasons: 1) Distributed SNNs could accommodate a broad range of applications because numerous scenarios require the integration of information collected from distributed sensors, with sensors and processors located in different places; 2) The distributed computing paradigm could fully utilize idle computation resources and enhance communication efficiency though collaboration [13], potentially enabling the development of large scale NNs [12]; 3) SNNs are suitable for distributed deployment because spiking neurons are event-driven and generate minimal amounts of data, which could significantly reduce the communication costs especially in wireless environments. Potential applications of distributed SNN may involve security alarms, environmental monitoring, intelligent robots [14], and healthcare [15].…”

Neuromorphic Wireless Cognition: Event-Driven Semantic Communications for Remote Inference

“…A recent paper [25] by a team at Ericsson has considered the distributed implementation of an SNN on a packet-based wireless channel. Unlike the present work, the SNN is trained offline in a channel-agnostic manner, and spikes are transmitted at the application level using packets.…”

“…Unlike the present work, the SNN is trained offline in a channel-agnostic manner, and spikes are transmitted at the application level using packets. The authors of [25] demonstrate the impact of packet losses and present a prototype implementation.…”