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引用次数: 0

摘要

PATMOS已有29年的历史,是世界上最早的低功耗设计会议之一。PATMOS系列会议的传统范围主要是关于以最低功耗优化最高性能的电路和系统设计。但与此同时,能效在许多其他领域变得极其重要,这些领域远远超出了传统的研发领域。在被称为物联网(IoT)的电池供电节点连接网络中,能源效率已成为必须的条件。可穿戴设备、家用电器、车辆和安全监控系统大多依赖于小型传感器,理想情况下,这些传感器在充电后可以运行数天。然而,目前的电池技术无法满足这些系统在功率方面日益增长的需求。此外,节能的ICT(信息和通信技术)基础设施是地方和全球经济的一个关键问题。根据目前的趋势,预计到2030年,由互联网引起的用电量将增长30倍。此外,无线通信的强劲增长和云计算的增长需要数量级的计算能力。PATMOS 2019的目的是超越现有的解决方案,从而缩小ICT基础设施计算与大量能源消耗之间的巨大差距。
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About PATMOS
PATMOS has a history of 29 years and it is one of the first conferences world-wide focusing on low power design. The traditional scope of PATMOS conference series has mainly been about the design of circuits and systems optimized for highest performance at lowest power consumption. But meanwhile, power-efficiency has become extremely important for many other areas spreading far beyond this traditional R&D niche. Energy efficiency has become a must in the connected network of battery-operated nodes known as Internet-of-Things (IoT). Wearable devices, home appliances, vehicles and security surveillance systems mostly rely on small sensors that should ideally operate on battery charge for days. However, current battery technology cannot meet the growing demands of these systems in terms of power. Also, energy-efficient ICT (Information and Communication Technology) infrastructures are a key issue of local and global economies. Based on the current trends, it is predicted that until the year 2030 the electricity consumption caused by the Internet will grow by up to 30 times. In addition, the strong increase of wireless communication and the growth of cloud computing require orders of magnitude more computational power. It is the intention of PATMOS 2019 to think beyond current solutions such that the wide gap between computation and the massive energy consumption for ICT infrastructures can be closed.
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On the Static CMOS Implementation of Magnitude Comparators [PATMOS 2019 Title Page] UVM-based Verification of a Digital PLL Using SystemVerilog Minimizing Power for Neural Network Training with Logarithm-Approximate Floating-Point Multiplier Stochastic Radial Basis Neural Networks
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