Brain-inspired AI for Edge Intelligence: a systematic review

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.

Figures (8)

• Figure 1: Overview of the paradigm shift from Traditional DNNs to Spiking Neural Networks (SNNs).(Top) Data Representation: DNNs process redundant frame-based image sequences, whereas SNNs process sparse, asynchronous event streams represented in a spatiotemporal volume ($x, y, t$). (Middle) Neuron Dynamics: Unlike static activation functions (e.g., Sigmoid) in DNNs, SNNs employ biologically plausible Leaky Integrate-and-Fire (LIF) neurons that integrate temporal information governed by differential equations. (Bottom) Energy Efficiency: The shift from dense MAC operations to event-driven Accumulate (AC) logic results in an ultra-low energy footprint, making SNNs highly suitable for edge intelligence.
• Figure 2: Hierarchical Organization of the Survey. The article is structured into four main pillars: Part I establishes the theoretical foundations and defines the "Deployment Paradox"; Part II explores the technical frontiers across algorithms, hardware, and toolchains (2020--2025); Part III examines real-world realization in key edge applications; and Part IV synthesizes challenges to propose a roadmap towards a unified Neuromorphic OS.
• Figure 3: Dataflow Contrast: Continuous vs. Event-Driven.(Left) DNN Dataflow: Relies on dense Matrix-Vector Multiplication (MAC), creating a continuous "Memory Wall" as weights are fetched unconditionally. (Right) SNN Dataflow: Utilizes a conditional trigger mechanism where memory access and computation occur only upon spike arrival. This sparsity-aware processing significantly reduces off-chip memory bandwidth requirements.
• Figure 4: Radar chart illustrating the multidimensional trade-offs between Neuromorphic SNNs and a traditional Edge DNN baseline. While the DNN maintains a marginal advantage in static accuracy, the SNN demonstrates superior performance in SWaP-constrained metrics—specifically Energy Efficiency, Sparsity, and Always-on Suitability. This architectural alignment identifies SNNs as the optimal candidate for resource-limited edge deployment despite the accuracy trade-off.
• Figure 5: Taxonomy of Brain-inspired Edge Intelligence. The survey is structured around four pillars: (1) Training Paradigms: Highlighting the shift from conversion to direct training algorithms like STBP and tdBN; (2) Neuromorphic Hardware: Covering native digital chips, FPGA accelerators, and hybrid architectures like Tianjic; (3) Co-design Strategies: Focusing on NAS and temporal quantization; (4) System & Applications: Emphasizing high-speed vision (Vidar) and edge-cloud orchestration.