Book excerpt: Improving the energy efficiency has been the critical design goal for embedded systems. Recently, there have been some practical techniques employed to the power supply of embedded systems to extend the system's lifetime. One is renewable energy technologies such as energy harvesting from the environment to offer a sustainable, inexpensive, and maintenance-free alternative power source. Another is voltage overscaling (VOS) technique, which scales down the supply voltage to reduce the power consumption quadratically. However, most renewable energy sources are unstable and intermittent due to dynamically changing environmental conditions, and VOS inevitably incurs hardware errors, thereby posing new challenges to the improvements of energy efficiency in the embedded systems. In this dissertation, we identify four specific power-hungry signal processing units and develop a suite of techniques to improve the energy efficiency of embedded systems, by jointly exploiting the properties of the power source and the domain-specific information in the signal processing of embedded systems. First, we propose to dynamically adjust the modulation scheme to deal with time-varying wireless channel conditions and non-deterministic renewable energy levels in a coherent manner to maximize the data rate of RF circuits of the embedded systems. Then, we develop a progressive performance tuning approach to dynamically determine an acceptable signal processing performance in accordance with the changing energy level at runtime, by considering both of the non-deterministic characteristics of renewable energy and the unique relationship between signal processing performance and the required energy consumption. We also develop a link and energy adaptive UWB-based sensing technique to improve the detection time coverage and range coverage for self-sustained embedded applications. The proposed technique jointly exploits the link information between the transmitter and receiver of the UWB pulse radar, and the non-deterministic characteristics of the renewable energy, and dynamically adjusts the pulse repetition frequency of the UWB radar to enhance the sustainable operation under the unreliable energy supply. Finally, we present a low-power LDPC decoder design by exploiting inherent memory error statistics due to voltage scaling. After analyzing the error sensitivity to the decoding performance at different memory bits and memory locations in the LDPC decoder, we apply the scaled supply voltage to memory bits with high algorithmic error-tolerance capability to reduce the memory power consumption with minimal decoding performance loss.