Approximate computing and the quest for computing efficiency

Diminishing benefits from technology scaling have pushed designers to look for new sources of computing efficiency. Multicores and heterogeneous accelerator-based architectures are a by-product of this quest to obtain improvements in the performance of computing platforms at similar or lower power budgets. In light of the need for new innovations to sustain these improvements, we discuss approximate computing, a field that has attracted considerable interest over the last decade. While the core principles of approximate computing - computing efficiently by producing results that are good enough or of sufficient quality - are not new and are shared by many fields from algorithm design to networks and distributed systems, recent e?orts have seen a percolation of these principles to all layers of the computing stack, including circuits, architecture, and software. Approximate computing techniques have also evolved from ad hoc and application-specific to more broadly applicable, supported by systematic design methodologies. Finally, the emergence of workloads such as recognition, mining, search, data analytics, inference and vision are greatly increasing the opportunities for approximate computing. We describe the vision and key principles that have guided our work in this area, and outline a holistic cross-layer framework for approximate computing.