Conventional techniques in energy-efficient computing navigate a design space defined by the two dimensions of performance and energy, and traditionally trade one for the other. General-purpose approximate computing explores a third dimension, error, and trades the accuracy of computation for gains in both energy and performance.
By relaxing the need for fully precise or completely deterministic operations, approximate computing techniques allow substantially improved energy efficiency. A team at the University of Illinois flagship campus recently published a paper that details how this technique can boost a bitcoin miners profits by 30%.
Led by Associate Professor of Electrical and Computer Engineering, Rakesh Kumar Ph.D., the three authors of “Approximate Bitcoin Mining” claim that the 6-page paper is the first to explore hardware optimizations, specifically approximation based optimizations, unique to Bitcoin mining.
“Mining is inherently error tolerant due to its embarrassingly parallel and probabilistic nature. We exploit this inherent tolerance to inaccuracy by proposing approximate mining circuits that trade off reliability with area and delay.”
– Approximate Bitcoin Mining
Approximation is not a new idea, and has been used in areas such as lossy compression and numeric computation. John