Neuromorphic Edge Computing: Challenges, Opportunities, and Current Solutions

作者：Federico Corradi, Amir Zjajo, L. M. Bolzani Poehls, Miloš Krstić, Orlando Moreira, Zeqi Zhu, Farhad Merchant · 年份：2025 · DOI：10.1109/islped65674.2025.11261768 · 被引用次数：3 · 研究领域：Advanced Memory and Neural Computing、Ferroelectric and Negative Capacitance Devices、Physical Unclonable Functions (PUFs) and Hardware Security

Neuromorphic computing is emerging as a paradigm for high-performance, energy-efficient edge intelligence. Yet the transition from laboratory prototypes to deployable edge platforms is slowed by four intertwined obstacles: (1) complex near-sensor integration, where spiking inference must co-locate with analogue sensing to minimise latency and data-movement energy; (2) novel event-based optimisation, requiring weight compression and sparsity techniques tailored to event-driven workloads; (3) heterogeneous integration of emerging devices, such as RRAM and other non-volatile memories, into reliable, manufacturable stacks; and (4) novel security risks, including spike-pattern side channels and model-specific attacks that demand to develop neuromorphic security primitives. This paper surveys the state of the art across these four fronts, drawing on recent advances in spiking microcontrollers, mixed-precision compute-in-memory fabrics, sparsity-aware compilation, and hardware-anchored security primitives (physical unclonable functions, true random number generators, computing-in-memory-based cryptography). By distilling lessons from academic research and industrial prototyping, the paper outlines current solutions and future research directions aimed at accelerating the adoption of neuromorphic platforms in real-world edge AI systems.