Approximate Computing: Concepts, Architectures, Challenges, Applications, and Future Directions

The unprecedented progress in computational technologies led to a substantial proliferation of artificial intelligence applications, notably in the era of big data and IoT devices. In the face of exponential data growth and complex computations, conventional computing encounters substantial obstacles pertaining to energy efficiency, computational speed, and area. Due to the diminishing advantages of technology scaling and increased demands from computing workloads, novel design techniques are required to increase performance and decrease power consumption. Approximate computing, nowadays considered a promising paradigm, achieves considerable improvements in overhead cost reduction (i.e., energy, area, and latency) at the expense of a modest (i.e., still acceptable) deterioration in application accuracy. Therefore, approximate computing at different levels (Data, Circuit, Architecture, and Software) has been attracted by the research and industrial communities. This paper presents a comprehensive review of the major research areas of different levels of approximate computing by exploring their underlying principles, potential benefits, and associated trade-offs. This is a burgeoning field that seeks to balance computational efficiency with acceptable accuracy. The paper highlights opportunities where these techniques can be effectively applied, such as in applications where perfect accuracy is not a strict requirement. This paper presents assessments of applying approximate computing techniques in various applications, especially machine learning algorithms (ML) and IoT. Furthermore, this review underscores the challenges encountered in implementing approximate computing techniques and highlights potential future research avenues. The anticipation is that this survey will stimulate further discourse and underscore the necessity for continued research and development to fully exploit the potential of approximate computing.