Approximate Computing

This book explores the technological developments at various levels of abstraction, of the new paradigm of approximate computing. The authors describe in a single-source the state-of-the-art, covering the entire spectrum of research activities in approximate computing, bridging device, circuit, architecture, and system levels. Content includes tutorials, reviews and surveys of current theoretical/experimental results, design methodologies and applications developed in approximate computing for a wide scope of readership and specialists.

• Serves as a single-source reference to state-of-the-art of approximate computing;
• Covers broad range of topics, from circuits to applications;
• Includes contributions by leading researchers, from academia and industry.

Part I Approximate Arithmetic and Circuits
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1-Approximate Arithmetic Circuits: Design and Applications. - 2-An Automated Logic Level Framework for Approximate Modular Arithmetic Circuits. - 3-Approximate Multiplier Design for Energy Efficiency: From Circuit to Algorithm. - 4-Low-Precision Floating-Point Formats: From General-Purpose to Application-Specific. - 5-Spintronic Solutions for Approximate Computing. - 6-Majority Logic Based Approximate Multipliers for Error-Tolerant Applications. -
Part II Design Automation and Test. -
7-Approximate Logic Synthesis for FPGA by Decomposition. - 8-Design Techniques for Approximate Realization of Data-Flow Graphs. - 9-Approximation on Data Flow Graph Execution for Energy Efficiency. - 10-Test and Reliability of Approximate Hardware. -
Part III Security. -
11-SecurityVulnerabilities in Approximate Circuits. - 12-Voltage Overscaling Techniques for Security Applications. - 13-Approximate Computing for Cryptography. - 14-Towards Securing Approximate Computing Systems: Security Threats and Attack Mitigation. -
Part IV Neural Networks and Machine Learning. -
15-Approximate Computing for Machine Learning Workloads: A circuits and systems perspective. - 16-Approximate Computing for Efficient Neural Network Computation. - 17-Enabling Efficient Inference of Convolutional Neural Networks via Approximation. - 18-Approximate Computing for Energy-Constrained DNN-based Speech Recognition. - 19-Efficient Approximate DNN Accelerators for Edge Devices. -
Part V Applications. -
20-Cross-Level Design of Approximate Computing for Continuous Perception System. - 21-Approximate Computing in Image Compression and Denoising. - 22-Approximate Computation for Baseband Processing.