Integration of neuromorphic AI in event-driven distributed digitized systems: Concepts and research directions

Abstract

Increasing complexity and data-generation rates in cyber-physical systems and the industrial Internet of things are calling for a corresponding increase in AI capabilities at the resource-constrained edges of the Internet. Meanwhile, the resource requirements of digital computing and deep learning are growing exponentially, in an unsustainable manner. One possible way to bridge this gap is the adoption of resource-efficient brain-inspired “neuromorphic” processing and sensing devices, which use event-driven, asynchronous, dynamic neurosynaptic elements with colocated memory for distributed processing and machine learning. However, since neuromorphic systems are fundamentally different from conventional von Neumann computers and clock-driven sensor systems, several challenges are posed to large-scale adoption and integration of neuromorphic devices into the existing distributed digital–computational infrastructure. Here, we describe the current landscape of neuromorphic computing, focusing on characteristics that pose integration challenges. Based on this analysis, we propose a microservice-based conceptual framework for neuromorphic systems integration, consisting of a neuromorphic-system proxy, which would provide virtualization and communication capabilities required in distributed systems of systems, in combination with a declarative programming approach offering engineering-process abstraction. We also present concepts that could serve as a basis for the realization of this framework, and identify directions for further research required to enable large-scale system integration of neuromorphic devices.

Neuromorphic applications in medicine

“…In traditional computing systems, individual components, such as transistors, can fail causing large portions of the system to malfunction. However, in neuromorphic hardware, multiple artificial neurons and synapses are used to perform the same function [29]. This redundancy helps build resilience to systems failing.…”

Artificial intelligence-based spatio-temporal vision sensors: applications and prospects

“…Datasets for neuromorphic vision sensors include (Zhu et al, 2018;Pfeiffer et al, 2022;Nilsson et al, 2023) for tasks like depth estimation and visual odometry. Using DAVIS346 to record a significant number of autopilot and UAV scenes, Zhu et al (Zhu et al, 2018) revealed the MVSEC dataset for stereo vision, which is extensively used in the field of neuromorphic vision.…”

“…Using DAVIS346 to record a significant number of autopilot and UAV scenes, Zhu et al (Zhu et al, 2018) revealed the MVSEC dataset for stereo vision, which is extensively used in the field of neuromorphic vision. A DAVIS240 and an Astra depth camera mounted on a mobile robot were used to record interior scenes in (Nilsson et al, 2023) large-scale multimodal LMED dataset. For the SLAM problem, Pfeiffer et al (Pfeiffer et al, 2022) created a UZH-FPV dataset with built-in information such as DAVIS346 pulse flow, APS image, optical flow, camera posture, and route (Paredes et al, 2019).…”

Optimizing event-based neural networks on digital neuromorphic architecture: a comprehensive design space exploration

“…Event-driven data-flow computation, inspired by the sparse spiking activities in cortical networks (Wolfe et al, 2010 ), is the primary computing paradigm in the majority of neuromorphic processors (Nilsson et al, 2023 ). It takes advantage of the sparsity in neural activation (spikes) to skip redundant operations.…”