Special Issue: Approximate Computing: From Circuits to Applications
Volume 108, Issue 12
Special Issue Papers
This article provides a comprehensive evaluation of recently proposed approximate arithmetic circuits mainly including adders, multipliers, and dividers; they are compared under different design constraints and applied to image processing and deep learning applications.
This article presents classical approaches of approximate elementary functions with the mainstream techniques of shift-and-add algorithms, polynomial or rational approximations, table-based methods, and bit manipulation.
This article examines the test procedure of approximate integrated circuits by identifying the main approximation-aware testing phases.
This article provides an overview of the recently proposed deep in-memory architectures (DIMAs) with several approximate prototype chips using 65-nm technology for hardware acceleration of machine learning algorithms.
This article presents a comprehensive overview of the emerging paradigm of approximate computing using NVM crossbars for accelerating machine learning workloads.