Approximate computing: An emerging paradigm for energy-efficient design

By Jie Han, Michael Orshansky. IEEE European Test Symposium, 2013

1,073 citations recorded by the source corpus.

How researchers see this paper

The researchers whose trusted networks rank it highest, strongest advocate first. Every researcher gets a different order.

A low-power, high-performance approximate multiplier with configurable partial error recovery (2014, background): “As one of the key components in arithmetic circuits, adders have been extensively studied for approximate implementation (see [1] for a review).”
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.”
Approximate Computing: A Survey (2016, result): “When compared to a recent survey [4], our taxonomy and organization are completely different and we take a much broader viewpoint that spans all computing layers.”
DRUM: A Dynamic Range Unbiased Multiplier for approximate applications (2015, background): “This error tolerance arises for multiple reasons, including imperfect perception in human sense, noisy input signal, redundancy in the input data or the absence of a universal best answer [1].”
Hardware implementation of memristor-based artificial neural networks (2024, neutral): “An alternative to keep a high throughput and still employ a low-resolution DAC is using approximate computing 101 .”