Exploiting assertions mining and fault analysis to guide RTL-level approximation.


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Approximate Computing (AxC) paradigm was introduced to achieve higher power efficiency, lower area and better performances w.r.t. a “classical” computing system at the cost of a degraded, but still acceptable, output accuracy [1]. AxC can be applied at several abstraction levels of a given computing system: from circuit to algorithm [1], leading to a wide design exploration space that quickly became the bottleneck for successfully deploying AxC. Indeed, the literature proposes many works to automatically trade-off between output accuracy and performances [2]. However, most of them lack the capability to identify resilient elements (e.g, HW component, HDL statements, etc.) of the design to be approximated. Consequently, exploring the design for AxC generally results in a long and tedious procedure. Existing approaches generate approximate variants of the Design Under Exploration (DUE). Every variant is then executed/simulated in order to determine the accuracy degradation [3], which depends on the application and requires a specific metric to be computed (e.g., similarity index, hamming distance, etc.).
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