Bringing Resource Efficiency to Smartphones with Approximate Computing

This research is funded by the Slovenian National Research Agency (ARRS) project number N2-0136 [SICRIS]

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The breakdown of Moore's law and Dennard scaling critically threatens the future growth of mobile computing. It indicates that further packing of computing resources cannot be sustained, should we wish to preserve the portability and energy frugality of our devices.

In this project we aim to set foundations for a new mobile computing paradigm termed - approximate mobile computing (AMC). AMC is based on our insight that, depending on the context of use, computation need not be perfectly accurate in order to fulfil a user’s needs.

Here is an example: a human activity recognition application can use only sporadic sparse sampling of built-in mobile sensors and still be able to recognise the activity, if the activity is “easy” to detect (e.g. still, sitting).

Bringing AMC to realisation requires that the underlying foundation - the technology for enabling accuracy-adaptable computation - is implemented. In the project we focus on:

Results of our work provide a basis for future experimentation with AMC, in particular with respect to its context-aware adaptation and further experimentation with resource savings that AMC brings.


Code and data: