Energy-quality scalable integrated circuits and systems: Continuing energy scaling in the twilight of Moore's law

This paper aims to take stock of recent advances in the field of energy-quality (EQ) scalable circuits and systems, as promising direction to continue the historical exponential energy downscaling under diminished returns from technology and voltage scaling. EQ-scalable systems explicitly trade off energy and quality at different levels of abstraction and sub-systems, dealing with 'quality' as an explicit design requirement, and reducing energy whenever the application, the task, or the dataset allow quality degradation (e.g., vision and machine learning). A general framework for EQ-scalable systems based on the concept of quality slack is presented along with scalable architectures. A taxonomy of techniques to trade off energy and quality, a VLSI perspective, and possible quality control strategies are then discussed. The state of the art is surveyed to put the advances in its different sub-fields into a unitary perspective, emphasizing the on-going and prospective trends. At the component level, the generality of the EQ-scaling concept is shown through several examples, ranging from logic to analog circuits, to memories, data converters, and accelerators. Interesting implications of the joint adoption of EQ scaling and machine learning are also discussed, suggesting that their synergy gives ample room for further energy and performance improvements. From a level of abstraction viewpoint, EQ scaling is discussed from the circuit level to architectures, the hardware-software interface, the programming language, the compiler level, and run-time adaptation. Several case studies are discussed to put EQ scaling in the context of real-world applications.