A Review on Large‐Scale Hardware and Software Platforms for Neuromorphic Computing

Abstract

Neuromorphic computing is emerging as a promising paradigm for sustainable edge intelligence by enabling event‐driven, low‐latency and energy‐aware computation close to sensors. However, the field remains fragmented across device technologies, mixed‐signal and digital hardware platforms, spiking neural network models, software frameworks, event‐stream processing tools, interoperability standards and benchmarking practices. This review provides a cross‐layer synthesis of contemporary neuromorphic computing platforms with particular emphasis on their relevance to scalable and sustainable edge deployment. The article organizes the neuromorphic ecosystem into interconnected layers spanning materials and devices, hardware architectures, software and interoperability tools, benchmark resources and application domains. Mixed‐signal platforms are analysed in terms of analog efficiency, accelerated neural dynamics, biological plausibility, calibration requirements, variability and reproducibility challenges. Digital neuromorphic processors are examined with respect to programmability, deterministic execution, routing fabrics, memory organization, software integration and deployment readiness. The review further discusses software frameworks, simulators, event‐data libraries, hardware‐mapping tools and intermediate representations that support model development, portability and cross‐platform evaluation. A central finding is that neuromorphic systems cannot be compared meaningfully using isolated metrics such as neuron count, chip power, latency, or throughput alone; fair evaluation requires explicit reporting of workload, event rate, model topology, mapping strategy, software stack, measurement boundary and deployment context. Accordingly, the article proposes a benchmarking and reporting perspective for neuromorphic edge intelligence that links accuracy, latency, energy efficiency, robustness and reproducibility. Thus, this review clarifies current progress, unresolved challenges and future directions for sustainable edge sensing, robotics, healthcare monitoring, smart infrastructure, industrial automation and distributed intelligent systems.