Probable cause

Approximate computing research seeks to trade-off the accuracy of computation for increases in performance or reductions in power consumption. The observation driving approximate computing is that many applications tolerate small amounts of error which allows for an opportunistic relaxation of guard bands (e.g., clock rate and voltage). Besides affecting performance and power, reducing guard bands exposes analog properties of traditionally digital components. For DRAM, one analog property exposed by approximation is the variability of memory cell decay times. In this paper, we show how the differing cell decay times of approximate DRAM creates an error pattern that serves as a system identifying fingerprint. To validate this observation, we build an approximate memory platform and perform experiments that show that the fingerprint due to approximation is device dependent and resilient to changes in environment and level of approximation. To identify a DRAM chip given an approximate output, we develop a distance metric that yields a two-orders-of-magnitude difference in the distance between approximate results produced by the same DRAM chip and those produced by other DRAM chips. We use these results to create a mathematical model of approximate DRAM that we leverage to explore the end-to-end deanonymizing effects of approximate memory using a commodity system running an image manipulation program. The results from our experiment show that given less than 100 approximate outputs, the fingerprint for an approximate DRAM begins to converge to a single, machine identifying fingerprint.