Flexible electronics, coupled with artificial intelligence, hold the potential to revolutionize robotics, wearable and healthcare devices1, human–machine interfaces2, and other emerging applications3,4. However, the development of flexible computing hardware that can efficiently execute neural-network-inference tasks using parallel computing remains a substantial challenge5. Here we present FLEXI, a thin, lightweight and robust flexible digital artificial intelligence integrated circuit to address this challenge. Our approach uses process-circuit-algorithm co-optimization and a digital dynamically reconfigurable compute-in-memory architecture. Key features include clock frequency operation of up to 12.5 MHz and power consumption as low as 2.52 mW, all while achieving subdollar-per-unit cost and an operational circuit yield of between approximately 70% and 92%. Our circuits can perform 1010 fixed and random multiplications without error, withstand over 40,000 bending cycles and maintain stable performance for a period exceeding 6 months. A one-shot on-chip neural network deployment eliminates the power consumption and latency associated with sequential weight writing, achieving up to 99.2% accuracy in temporal arrhythmia detection tasks on a single 1-kb chip. In addition, FLEXI demonstrates over 97.4% accuracy in human daily activity monitoring using multimodal physiological signals.
