In line with the rapid development of the Internet of Things (IoT), the maintenance of on-board batteries for a trillion sensor nodes has become prohibitive both in time and costs. Energy harvesting is a promising solution to this problem. However, conventional energy-harvesting systems with storage suffer from low efficiency because of conversion loss and storage leakage. Direct supply systems without energy buffer provide higher efficiency, but fail to satisfy quality of service (QoS) due to mismatches between input power and workloads. Recently, a novel dual-channel photovoltaic power system has paved the way to achieve both high energy efficiency and QoS guarantee. This article focuses on the design-time and run-time co-optimization of the dual-channel solar power system. At the design stage, we develop a task failure rate estimation framework to balance design costs and failure rate. At run-time, we propose a task failure rate aware QoS tuning algorithm to further enhance energy efficiency. Through the experiments on both a simulation platform and a prototype board, this study demonstrates a 27% task failure rate reduction compared with conventional architectures with identical design costs. And the proposed online QoS tuning algorithm brings up to 30% improvement in energy efficiency with nearly zero failure rate penalty.