Titlebook: Approximate Computing; Weiqiang Liu,Fabrizio Lombardi Book 2022 The Editor(s) (if applicable) and The Author(s), under exclusive license t

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Approximate Computing for Cryptography Generally, it has been employed in a lot of error-tolerant applications such as image/multimedia signal processing, machine learning, etc., applications that allow accuracy degradation without quality degradation. But, approximation has also the potential being utilized to provide area and power ef

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Towards Securing Approximate Computing Systems: Security Threats and Attack Mitigationecent literature indicates that some AC mechanisms could be exploited by attackers to implement new attack surfaces. In this chapter, we introduce unique attacks that are applicable to AC systems and provide examples of practical attacks. Furthermore, we introduce general principles of defense mecha

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Approximate Computing for Machine Learning Workloads: A Circuits and Systems Perspective such as signal processing, machine learning (ML), and embedded systems. To reap maximum energy benefits as well as ensure high quality of solution for applications, innovations are needed across the entire computing stack (from circuits and architectures all the way up to algorithms). This chapter

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https://doi.org/10.1007/978-3-642-17033-1mitives such as multipliers, adders, memories, and matrix vector multiplication units which are vital to build a machine learning accelerator. Several algorithm techniques that can utilize the aforementioned hardware level approximations to accelerate machine learning workloads are also discussed.