This thesis presents groundbreaking contributions in the fields of intelligent neural interfaces and microscopic machines, specifically focusing on applications in medical implants and autonomous microscopic robots, utilizing CMOS electronic chips. In Chapter 2, an innovative ASIC accelerator is introduced, tailored for the efficient implementation of large decision tree prediction models, highlighting its unparalleled classification efficiency. Furthermore, we propose enhancements to further curtail redundancy during inference, showcasing the potential of CMOS technology to elevate scalability and adaptability in machine learning hardware. In Chapter 3, we present a novel method for achieving autonomous coordinated emergent behaviors in microscopic machines through pulse-coupling CMOS oscillators. A comprehensive analysis covers synchronization quality, scalability, and robustness, complemented by demonstrations of emergent behaviors in Matlab and realistic locomotion simulations in Unity. Chapter 4 concludes the thesis with reflections on the distinctive contributions and the wide horizon of potentials that these advancements unleash across diverse applications, ranging from transformative healthcare solutions to revolutionary advancements in intelligent and autonomous microscopic systems.