Brain-inspired AI for Edge Intelligence: a systematic review

TL;DR

This systematic review examines the hardware-software co-design challenges and technological advancements in brain-inspired AI, particularly Spiking Neural Networks, for efficient edge intelligence from 2020 to 2025.

Contribution

It provides a comprehensive system-level analysis of neuromorphic hardware and software integration, highlighting key bottlenecks and proposing a roadmap for future development.

Findings

Identifies the 'Deployment Paradox' where energy gains are negated by implementation inefficiencies.

Dissects the training complexity and memory bottlenecks in neuromorphic systems.

Proposes development of a standardized Neuromorphic OS to address sync-async mismatch.

Abstract

While Spiking Neural Networks (SNNs) promise to circumvent the severe Size, Weight, and Power (SWaP) constraints of edge intelligence, the field currently faces a "Deployment Paradox" where theoretical energy gains are frequently negated by the inefficiencies of mapping asynchronous, event-driven dynamics onto traditional von Neumann substrates. Transcending the reductionism of algorithm-only reviews, this survey adopts a rigorous system-level hardware-software co-design perspective to examine the 2020-2025 trajectory, specifically targeting the "last mile" technologies - from quantization methodologies to hybrid architectures - that translate biological plausibility into silicon reality. We critically dissect the interplay between training complexity (the dichotomy of direct learning vs. conversion), the "memory wall" bottlenecking stateful neuronal updates, and the critical software gap in neuromorphic compilation toolchains. Finally, we envision a roadmap to reconcile the fundamental "Sync-Async Mismatch," proposing the development of a standardized Neuromorphic OS as the foundational layer for realizing a ubiquitous, energy-autonomous Green Cognitive Substrate.