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proceedings of the ieee cover dec 2020
proceedings of the ieee cover dec 2020

Special Issue: Approximate Computing: From Circuits to Applications

Volume 108, Issue 12

December 2020

Guest Editors

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. Pedra

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.

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.

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.

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