Computing technology has been a backbone of our society. Its importance is hard to overemphasize. Today, we again confirm its extreme importance with recent advances in deep neural networks. Those emerging workloads impose an unprecedented amount of arithmetic complexity and data access beyond our existing computing systems can barely handle. Particularly, mobile and embedded computing systems will face a major challenge in achieving energy-efficient computing for truly enabling intelligent systems. In this talk, we will discuss the emerging analog and mixed-signal circuit techniques to improve energy efficiency. We will discuss two recent cases using such techniques, one on the speech recognition processor in hybrid analog and digital circuits and the other on the embedded SRAM circuits that support analog-mixed-signal in-memory (in-bitcell) computing for convolutional and deep neural networks.