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Abstract-Microelectronic circuits exhibit increasing variations in performance, power consumption, and reliability parameters across the manufactured parts and across use of these parts over time in the field. These variations have led to increasing use of overdesign and guardbands in design and test to ensure yield and reliability with respect to a rigid set of datasheet specifications. This paper explores the possibility of constructing computing machines that purposely expose hardware variations to various layers of the system stack including software. This leads to the vision of underdesigned hardware that utilizes a software stack that opportunistically adapts to a sensed or modeled hardware. The envisioned underdesigned and opportunistic computing (UnO) machines face a number of challenges related to the sensing infrastructure and software interfaces that can effectively utilize the sensory data. In this paper, we outline specific sensing mechanisms that we have developed and their potential use in building UnO machines.

Previous studies have demonstrated the advantages of single-ISA heterogeneous multi-core architectures for power and performance. However, none of those studies examined how to design such a processor; instead, they started with an assumed combination of pre-existing cores.This work assumes the flexibility to design a multi-core architecture from the ground up and seeks to address the following question: what should be the characteristics of the cores for a heterogeneous multi-processor for the highest area or power efficiency? The study is done for varying degrees of thread-level parallelism and for different area and power budgets.The most efficient chip multiprocessors are shown to be heterogeneous, with each core customized to a different subset of application characteristics -no single core is necessarily well suited to all applications. The performance ordering of cores on such processors is different for different applications; there is only a partial ordering among cores in terms of resources and complexity. This methodology produces performance gains as high as 40%. The performance improvements come with the added cost of customization.

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