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Register a new userMoores Law in CLEAR Light
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Added by
Shuai Sun
Publication date
2016
fields
Informatics Engineering
and research's language is
English
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The inability of Moores Law and other figure-of-merits (FOMs) to accurately explain the technology development of the semiconductor industry demands a holistic merit to guide the industry. Here we introduce a FOM termed CLEAR that accurately postdicts technology developments since the 1940s until today, and predicts photonics as a logical extension to keep-up the pace of information-handling machines. We show that CLEAR (Capability-to-Latency-Energy-Amount-Resistance) is multi-hierarchical applying to the device, interconnect, and system level. Being a holistic FOM, we show that empirical trends such as Moores Law and the Makimotos wave are special cases of the universal CLEAR merit. Looking ahead, photonic board- and chip-level technologies are able to continue the observed doubling rate of the CLEAR value every 12 months, while electronic technologies are unable to keep pace.
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As an emerging technology, blockchain has achieved great success in numerous application scenarios, from intelligent healthcare to smart cities. However, a long-standing bottleneck hindering its further development is the massive resource consumption attributed to the distributed storage and computation methods. This makes blockchain suffer from insufficient performance and poor scalability. Here, we analyze the recent blockchain techniques and demonstrate that the potential of widely-adopted consensus-based scaling is seriously limited, especially in the current era when Moores law-based hardware scaling is about to end. We achieve this by developing an open-source benchmarking tool, called Prism, for investigating the key factors causing low resource efficiency and then discuss various topology and hardware innovations which could help to scale up blockchain. To the best of our knowledge, this is the first in-depth study that explores the next-generation scaling strategies by conducting large-scale and comprehensive benchmarking.
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