Approximate computing for emerging technologies : Trading computational accuracy for energy efficiency

CMOS is a technology that has been around for many years. Because of its low cost and high availability, it is highly optimized and the most used transistor alternative for computers. As CMOS has the drawback of only being able to store binary data and as there will be a time when current technology will not be improved any further for technical or economical reasons, one efficient alternative is to use other transistor technologies that are able to store more than two states per cell. Doing so is however more fragile than before. That is, because having more than two states per cell tends to have a higher probability for misinterpretations than in binary systems. Also, it is harder to determine the original state after an eventual error. In some practical areas, however, errors might be acceptable to a certain level. For example, if the error results in a misclassified point in a data mining operation, 100 wrong pixels during a full HD movie or one slightly wrong color hue in a picture, this might be a good trade-off for significant gains in energy efficiency. The aim of this thesis is to classify certain data as "approximate" and "precise", using a memory model to distinguish these in cache- and main memory. By simulating the according behavior and letting errors be introduced during runtime to the approximate data, one may draw conclusions how error resilient different types of run code are. Results show that for simulated applications, up to 17.08% cache power can be saved by letting parts of the program be approximate and that some applications shows high error resilience in approximate environments