New Approaches for Power Binning of High Performance Microprocessors

The significant process parameter variations occurring during fabrication of high performance sequential circuits, such as microprocessors, are posing relevant uncertainties on the power that such circuits will consume in the field, while executing workloads typical for the diverse products they are oriented to (e.g., cellular phones, notebooks, servers, etc). On the other hand, different kinds of products have different constraints on the maximal power that could be consumed during the execution of typical workloads, due to diverse needs in terms of charge autonomy, heat dissipation, etc. Consequently, the power that will be consumed by microprocessors during the execution of typical workloads in the field needs to be accurately characterized at the end of fabrication. Such a power consumption characterization (hereinafter referred to as “power binning”), will enable to classify microprocessors in “power bins”, each one containing microprocessors suitable for different kinds of products, thus enabling to introduce them all into the market for different kinds of products. Based on these considerations, in this paper we propose an approach to characterize accurately at the end of fabrication, and at low-cost (in terms of characterization time), the power that microprocessors will consume in the in-field during the execution of workloads typical for different kinds of products. Our approach exploits scan-based Logic Built-In Self-Test (LBIST) to apply to microprocessors' sequential blocks test vectors that induce on their internal nodes an activity factor (AF) similar to that experienced during the in-field execution of workloads typical for different kinds of products, thus enabling to perform power binning by simply measuring their consumed power. Our approach enables to scale the AF from 0 percent up to 97.6 percent (on average for the considered benchmark circuits) compared to conventional LBIST, with a granularity of the 2 percent, thus enabling to emulate accurately the AF induced by workloads typical of a wide range of products. We propose a hardware implementation for our approach requiring a limited area overhead (lower than 3 percent) over conventional LBIST.