MACISH: Designing Approximate MAC Accelerators with Internal-Self-Healing

Ghayoor Gillani, M.A. Hanif, Bart Verstoep, Sabih H. Gerez, M. Shafique, Andre B.J. Kokkeler

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    24 Citations (Scopus)
    143 Downloads (Pure)


    Approximate computing studies the quality-efficiency trade-off to attain a best-efficiency (e.g., area, latency, and power) design for a given quality constraint and vice versa. Recently, self-healing methodologies for approximate computing have emerged that showed an effective quality-efficiency tradeoff as compared to the conventional error-restricted approximate computing methodologies. However, state-of-the-art self-healing methodologies are constrained to highly parallel implementations with similar modules (or parts of a datapath) in multiples of two and for square-accumulate functions through the pairing of mirror versions to achieve error cancellation. In this article, we propose a novel methodology for InternalSelf-Healing (ISH) that allows exploiting self-healing within a computing element internally without requiring a paired, parallel module, which extends the applicability to irregular/asymmetric datapaths while relieving the restriction of multiples of two for modules in a given datapath, as well as going beyond
    square functions. We employ our ISH methodology to design an approximate multiply-accumulate (xMAC), wherein the multiplier is regarded as an approximation stage and the accumulator as a healing stage. We propose to approximate a recursive multiplier in such a way that a near-to-zero average error is achieved for a given input distribution to cancel out the error at an accurate accumulation stage. To increase the efficacy of such a multiplier, we propose a novel 2 × 2 approximate multiplier design that alleviates the overflow problem within an n × n approximate recursive multiplier. The proposed ISH methodology shows a more effective quality-efficiency trade-off for an xMAC as compared to the conventional error-restricted methodologies for random inputs and for radio-astronomy calibration processing (up to 55% better quality output for equivalent-efficiency designs).
    Original languageEnglish
    Article number8727537
    Pages (from-to)77142-77160
    Number of pages19
    JournalIEEE Access
    Publication statusPublished - 31 May 2019


    • Approximate computing
    • multiply-accumulate (MAC) accelerator
    • internal-self-healing methodology
    • radio astronomy processing
    • power efficiency


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