TinyML-CoDE: A Co-Design and Evaluation Framework for Systematic TinyML Development

The proliferation of Tiny Machine Learning (TinyML) in resource-constrained Internet of Things (IoT) devices promises ubiquitous intelligence at the edge, yet its development remains ad hoc, fragmented, and difficult to scale. Based on a systematic review of 35 peer-reviewed papers (2020–2025), we identify five core research gaps: (G1) absence of standardized benchmarking across accu-racy, latency, memory, and energy; (G2) lack of systematic hardware-software co-design; (G3) neglected lifecycle management beyond initial deployment; (G4) insufficient security integration for resource-constrained devices; and (G5) toolchain interoperability barriers. To address these gaps, we propose TinyML-CoDE – a Co-Design and Evaluation framework that integrates constraint-aware specification, hardware-model recommendation, automated optimization, unified benchmarking, and lifecycle management into a coherent workflow. The frame-work’s projected benefits include a 30% reduction in development time, 25% improvement in energy efficiency, and 20% higher deployment success rate. We detail the framework’s architecture, algorithms, implementation roadmap, and validation strategy. TinyML-CoDE offers a systematic pathway to eliminate cur-rent fragmentation and enable scalable, reproducible, and maintainable TinyML deployments in industrial monitoring, healthcare, and environmental sensing.