Recent studies showed that DRAM restore time degrades as technology scales, which imposes large performance and energy overheads. This problem, prolonged restore time (PRT), has been identified by the DRAM industry as one of three major scaling challenges.This paper proposes DrMP, a novel fine-grained precision-aware DRAM restore scheduling approach, to mitigate PRT. The approach exploits process variations (PVs) within and across DRAM rows to save data with mixed precision. The paper describes three variants of the approach: DrMP-A, DrMP-P, and DrMP-U. DrMP-A supports approximate computing by mapping important data bits to fast row segments to reduce restore time for improved performance at a low application error rate. DrMP-P pairs memory rows together to reduce the average restore time for precise computing. DrMP-U combines DrMP-A and DrMP-P to better trade performance, energy consumption, and computation precision. Our experimental results show that, on average, DrMP achieves 20% performance improvement and 15% energy reduction over a precision-oblivious baseline. Further, DrMP achieves an error rate less than 1% at the application level for a suite of benchmarks, including applications that exhibit unacceptable error rates under simple approximation that does not differentiate the importance of different bits.