Special Issue Papers
By W. Liu, F. Lombardi, and M. Schulten
By H. Jiang, F. J. H. Santiago, H. Mo, L. Liu, and J. Han
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.
By J.-M. Muller
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.
By S. Amanollahi, M. Kamal, A. Afzali-Kusha, and M. Pedram
This article reviews the circuit-level techniques for both approximate logic and memory blocks.
By M. Traiola, A. Virazel, P. Girard, M. Barbareschi, and A. Bosio
This article examines the test procedure of approximate integrated circuits by identifying the main approximation-aware testing phases.
By I. Scarabottolo, G. Ansaloni, G. A. Constantinides, L. Pozzi, and S. Reda
This article reviews the transformation methods for functional approximation, which can achieve an approximate Boolean function from its exact designs.
Security in Approximate Computing and Approximate Computing for Security: Challenges and Opportunities
By W. Liu, C. Gu, M. O’Neill, G. Qu, P. Montuschi, and F. Lombardi
This article focuses on an emerging area that links security and approximate computing.
By S. Venkataramani et al.
This article presents IBM’s RAPID, which is a multi-TOPs DNN hardware accelerator core fabricated using 14-nm technology.
By M. Kang, S. K. Gonugondla, and N. R. Shanbhag
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.
Resistive Crossbars as Approximate Hardware Building Blocks for Machine Learning: Opportunities and Challenges
By I. Chakraborty, M. Ali, A. Ankit, S. Jain, S. Roy, S. Sridharan, A. Agrawal, A. Raghunathan, and K. Roy
This article presents a comprehensive overview of the emerging paradigm of approximate computing using NVM crossbars for accelerating machine learning workloads.