Demand for low-power data processing hardware continues to rise inexorably. Existing programmable and "general purpose" solutions (eg. SIMD, GPGPUs) are insufficient, as evidenced by the order-of-magnitude improvements and industry adoption of application and domain-specific accelerators in important areas like machine learning, computer vision and big data. The stark tradeoffs between efficiency and generality at these two extremes poses a difficult question: how could domain-specific hardware efficiency be achieved without domain-specific hardware solutions?
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Nowatzki et al. (2017) studied this question.
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