Remarkable advances in Artificial Intelligence (AI) have demonstrated near-human cognitive performance in various applications. However, state-of-the-art AI still exhibits a large (orders of magnitude) efficiency gap compared to human brains. Enabling efficient AI hardware/software systems will be the key to deploying AI in various domains, including transportation, healthcare, and defense. Taking cues from the biological brains, neuro-inspired computing recently emerges as a promising approach to addressing the computational efficiency challenges. However, neuro-inspired computing with the complementary metal-oxide-semiconductor (CMOS) digital hardware lacks flexibility and efficiency due to mismatch at various levels from device to architecture. This project will leverage novel magneto-electronic (spintronic) technologies to create efficient and robust computational components that emulate neural stochastic functionality. The components will be integrated into in-memory computing architectures and co-designed with bio-inspired learning algorithms to achieve advanced cognitive capabilities. This project will significantly advance the science of developing next-generation AI hardware with emerging technologies. By implementing device-to-system co-design for stochastic in-memory computing, this project will create interdisciplinary knowledge of device integration, computing architecture design, and algorithm development. Such knowledge is crucial for addressing the challenges