Analysis and characterization of inherent application resilience for approximate computing

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A low latency generic accuracy configurable adder (2015, background): “Recently, the research by Intel [1], Microsoft [2], and other groups [3][5] has shown that there is a large body of resource-hungry applications from prevalent and emerging application domains (like: data analytics and data mining, image/video processing, computer vision, mobile computing, artificia”
Approximate Computing: A Survey (2016, background): “Fortunately, such applications usually feature an intrinsic error-resilience property [2].”
Reduced-Precision Strategies for Bounded Memory in Deep Neural Nets (2015, background): “Promisingly, training with reduced precision has been shown to increase the network’s tolerance to the error from reduced precision Chippa et al. (2013), however, care must be taken to ensure convergence.”
Mobile Augmented Reality Survey: From Where We Are to Where We Go (2017, background): “In [287], it describes a framework to facilitate application resilient characterization (ARC) to bring approximate computing closer to mainstream adoption so future adopters of this technique can analyze and characterize the resilience of their applications.”
Efficient embedded learning for IoT devices (2016, methodology): “To address these challenges, several approximation techniques at various layers of the computing stack, including software [17]–[20], architecture [12], [21]–[24] and circuits [25]–[29], have been proposed.”