Approximate computing has been proposed as a novel paradigm for efficient and low power design at nanoscales. It generates results that are good enough rather than always fully accurate and is thus suitable for applications that have inherent error resilience such as multimedia, signal processing, machine learning, and pattern recognition. Many of these applications are based on statistical or probabilistic computation such that different approximations can be made to better suit the desired objectives. It is, therefore, possible to achieve not only energy efficiency but also simpler design and lower latency while relaxing the accuracy requirement for these applications. Although approximate computing has gained significant attention from both academic and industrial communities in the past decade, it still requires further efforts to make it a mainstream computing paradigm for energy-efficient and high-performance systems. This special issue aims to provide a state-of-the-art coverage of research in approximate computing by including research activities across device, circuit, architecture, and system levels. The articles will be specifically geared toward specialists as well as a wider readership and will cover current theoretical/experimental results, design methodologies, and applications developed in approximate computing.