This article reports a keyword-spotting (KWS) chip for voice-controlled devices. It features a number of techniques to enhance the performance, area, and power efficiencies: 1) a fast-sampling convolutional neural network (FS-CNN) that eliminates the power-hungry feature extractors and reduces the decision latency; 2) an always-retention 5T-SRAM that features word-voltage switches to reduce the leakage power and single bitline (BL) operation to halve the SRAM read power compared to the typical 6T-SRAM; and 3) a high-resolution sparsity-aware computing (HR-SAC) unit that enhances the precision and output swing of the multiply–accumulate (MAC) computation. Benchmarking with the state-of-the-art, our KWS chip prototyped in 28-nm CMOS scores a $>$ 90% accuracy for the 11-class Google speech command dataset (GSCD) at 2.91 $\mu$ W, which corresponds to a 2.91-nJ energy/decision. The achieved latency is 2 ms/decision, and the core area is 0.05 ${\rm mm}^{2}$ , including the full KWS model.