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As edge devices see increasing adoption across a wide range of applications, understanding their environmental impact has become increasingly urgent. Unlike cloud systems, edge deployments consist of tightly integrated microcontrollers, sensors, and energy sources that collectively shape their carbon footprint. In this paper, we present a carbon-aware design framework tailored to embedded edge systems. We analyze the embodied emissions of several off-the-shelf microcontroller boards and peripheral components and examine how deployment context—such as workload type, power source, and usage duration—alters the carbon-optimal configuration. Through empirical case studies comparing battery- and solar-powered scenarios, we find that the lowest-emission choice is often workload- and context-specific, challenging assumptions that energy-efficient or renewable powered systems are always the most sustainable. Our results highlight the need for fine-grained, system-level reasoning when designing for sustainability at the edge and provide actionable insights for researchers and practitioners seeking to reduce the carbon cost of future deployments. 

Task-based intermittent software systems always re-execute peripheral input/output (I/O) operations upon power failures since tasks have all-or-nothing semantics. Re-executed I/O wastes significant time and energy and risks memory inconsistency. This paper presents EaseIO, a new task-based intermittent system that remedies these problems. EaseIO programming interface introduces re-execution semantics for I/O operations to facilitate safe and efficient I/O management for intermittent applications. EaseIO compiler front-end considers the programmer-annotated I/O re-execution semantics to preserve the task's energy efficiency and idem-potency. EaseIO runtime introduces regional privatization to eliminate memory inconsistency caused by idempotence bugs. Our evaluation shows that EaseIO reduces the wasted useful I/O work by up to 3× and total execution time by up to 44% by avoiding 76% of the redundant I/O operations, as compared to the state-of-the-art approaches for intermittent computing. Moreover, for the first time, EaseIO ensures memory consistency during DMA-based I/O operations.