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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Design: Was das ist und was es bringt,hensive investigation of approximate unsigned and signed multiplier designs based on ML. Approximate partial product generation, reduction, and compression are discussed, specifically with some complementary strategies guided by an analysis of error effects to compensate for the accuracy loss. The a

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https://doi.org/10.1007/978-3-322-84696-9 dependencies. Completion time and energy consumption are two main objectives for DFG optimization. In this chapter, we discuss approximation methods at different levels of DFG that can reduce energy consumption with a guaranteed quantity of results. First, we consider a probabilistic design framewo

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https://doi.org/10.1007/978-3-322-82395-3s. It provides significant benefits for energy-efficient systems and is being considered for high speed and low power nanoscale integrated circuit (IC) designs. It is crucial for ICs to achieve high speed and low power, where some intrinsic errors are acceptable, such as (deep-) machine learning, im

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https://doi.org/10.1007/978-3-642-17033-1 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