On-Device Intelligence: Local Processing Architecture for Mobile Computing Systems

Abstract

Edge intelligence fundamentally transforms mobile computing architectures by relocating artificial intelligence processing from centralized cloud infrastructure to distributed mobile devices. Traditional cloud-dependent systems encounter inherent limitations, including network latency that precludes real-time interaction, privacy vulnerabilities arising from centralized data aggregation, and accessibility constraints in connectivity-limited regions. The architectural transition to on-device processing addresses these challenges through specialised neural processing hardware implementing highly parallel computational structures optimised for matrix operations and convolutions characteristic of deep learning workloads. This paper makes four primary contributions to understanding edge intelligence architectures. First, systematic examination of specialised neural processing hardware reveals that memory hierarchy design, rather than computational throughput, often determines overall energy efficiency for mobile neural inference. Second, analysis of federated learning augmented with differential privacy mechanisms demonstrates that collaborative model development need not compromise data sovereignty while revealing fundamental trade-offs between privacy strength and model utility. Third, detailed examination of healthcare monitoring, accessibility technologies, and personalized recommendation systems characterizes distinct requirements for local processing, providing quantitative benchmarks: healthcare applications require latency below 100ms for safety-critical detection, accessibility applications demand processing under 20ms to avoid perceptual lag, and personalization systems tolerate latency up to 500ms. Fourth, adaptive hybrid architectures dynamically partition computation between device and cloud, incorporating neural architecture search and once-for-all training paradigms that enable efficient deployment across heterogeneous hardware platforms. The convergence of hardware specialisation, algorithmic optimisation, and privacy-preserving architectures establishes edge intelligence as a viable alternative to cloud-centric artificial intelligence systems.