Similarly, occasional dropping of some frames in a video application can go undetected due to perceptual limitations of humans.
Approximate computing is based on the observation that in many scenarios, although performing exact computation requires large amount of resources, allowing bounded approximation can provide disproportionate gains in performance and energy, while still achieving acceptable result accuracy.
Therefore, approximate computing is mostly driven by applications that are related to human perception/cognition and have inherent error resilience.
Many of these applications are based on statistical or probabilistic computation, such as different approximations can be made to better suit the desired objectives.
[21] One notable application in machine learning is that Google is using this approach in their Tensor processing units (TPU, a custom ASIC).