Approximate computing that works on less precise data leads to significant performance gains and energy-cost reductions for compute kernels. However, without leveraging the full-stack design of computer systems, modern computer architectures undermine the potential of approximate computing. In this paper, we present Varifocal Storage, a dynamic multiresolution storage system that tackles challenges in performance, quality, flexibility and cost for computer systems supporting diverse application demands. Varifocal Storage dynamically adjusts the dataset resolution within a storage device, thereby mitigating the performance bottleneck of exchanging/preparing data for approximate compute kernels. Varifocal Storage introduces Autofocus and iFilter mechanisms to provide quality control inside the storage device and make programs more adaptive to diverse datasets. Varifocal Storage also offers flexible, efficient support for approximate and exact computing without exceeding the costs of conventional storage systems by (1) saving the raw dataset in the storage device, and (2) targeting operators that complement the power of existing SSD controllers to dynamically generate lower-resolution datasets. We evaluate the performance of Varifocal Storage by running applications on a heterogeneous computer with our prototype SSD. The results show that Varifocal Storage can speed up data resolution adjustments by 2.02× or 1.74× without programmer input. Compared to conventional approximate-computing architectures, Varifocal Storage speeds up the overall execution time by 1.52×.

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