A Survey of Techniques for Approximate Computing

By Sparsh Mittal. ACM Computing Surveys, 2016

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Mobile Augmented Reality Survey: From Where We Are to Where We Go (2017, background): “[283], Mittal [284], and Han and Orshansky [285] describe the reasons behind approximate computing and explore the current approximation techniques in hardware and software.”
EvoApproxSb: Library of approximate adders and multipliers for circuit design and benchmarking of approximation methods (2017, background): “Approximate circuits are becoming a viable alternative to conventional accurately operating circuits if energy efficiency is crucial and target application is error resilient [1].”
Dual Dynamic Inference: Enabling More Efficient, Adaptive, and Controllable Deep Inference (2019, neutral): “computing” [43] in order to output the best possible result with a stringent and potentially time-varying resource limit (even that result considerably degrades compared with the full inference”
A survey of FPGA-based accelerators for convolutional neural networks (2018, background): “CNNs contain signiﬁcant redundancy and are used for error-tolerant applications [79].”
Brian 2: an intuitive and efficient neural simulator (2019, methodology): “…of heterogeneous computing architectures such as GPUs, FPGAs, and even more specialised hardware (Fidjeland et al., 2009; Richert et al., 2011; Brette & Goodman, 2012; Moore et al., 2012; Furber et al., 2014; Cheung et al., 2016), as well as techniques such as approximate computing (Mittal, 2016).”