Towards a physical theory of computer science.

经验似乎表明，每当我们想要计算某事时，我们都需要使用一些能量来做到这一点。这可能是我们大脑的新陈代谢能量，对于电子计算机来说，可能是电，或者对于机械计算器来说，可能是储存在弹簧中的能量。一次计算到底消耗多少能量取决于所使用的硬件的具体情况。人们可能会问一个有趣的问题：对于任何给定的计算，执行它所需的最低能量是多少，而不考虑对硬件的任何假设？在一定程度上，这个问题在20世纪80年代得到了回答，得到了一个令人惊讶的结果：原则上，计算可以在完全不消耗能量的情况下进行。这种洞察力伴随着一个重要的警告。只有当一个人准备等待无限的时间来完成计算时，才能达到零能量限制。在实践中，这显然没有用。另一方面，在有限时间内完成的计算需要消耗正量的能量。因此，这种计算的功率消耗必须有一个非零的下限。目前，这一限制是什么仍然是一个悬而未决的问题。这个学科跳跃项目的目标是建立严格的计算物理模型，以探索他们的最低能量需求。具体地说，该项目将使用非平衡统计力学中新开发的方法，并将它们应用于理论计算机科学的概念。该项目将允许PI与卢森堡大学的Massimiliano Esposito教授小组共度4个月。在那里，他将能够获得最先进的随机热力学技术技能，这将使他能够完成这里描述的项目，并启动旨在建立计算物理极限的研究计划。该项目的主要成果将是描述计算能耗的物理限制的一系列模型。该项目的主题具有高度的社会相关性。与计算相关的活动现在占全球总能源消耗的10%以上。可以合理地假设，未来因计算而产生的绝对功耗将随着经济增长而呈指数级增长。这是不可持续的，需要从根本上提高计算机的效率。目前，这种改进主要是由硬件和软件的增量优化推动的。为了维持呈指数级增长的计算需求，需要改变游戏规则的新能源效率技术在超低功率下运行，接近可能的极限。虽然这个项目不会直接导致这样的新技术，但它将提供对计算中能量耗散原因的深刻理解。因此，它将支持未来的工程努力，旨在为当前的计算能源危机找到解决方案。重要的是，该项目的结果还将提供一个基准，以评估当前硬件技术的能效与理论上的最佳水平。

Experience seems to suggest that whenever we want to compute something, then we need to use some energy to do so. This could be the metabolic energy of our brains, electricity in the case of electronic computers or perhaps the energy stored in a spring in the case of mechanical calculating machines. Precisely how much energy a computation consumes depends on the specifics of the hardware used. An intriguing question one may ask is: For any given computation, what is the minimal amount of energy required to perform it, independently of any assumptions about the hardware? Partially, this question has been answered in the 1980s with a surprising result: In principle, computation can be performed with no energy expenditure all. This insight comes with an important caveat. The zero energy limit can only be reached if one is prepared to wait for an infinite amount of time for the computation to complete. In practice, this is clearly not useful. Computations that complete within a finite time, on the other hand, require a positive amount of energy to be expended. It follows that there must be a non-zero lower limit to the power consumption of such computations. Currently, it remains an open question what this limit is. The objective of this discipline hopping project is to establish rigorous physical models of computation so as to probe their minimal energy requirements. Specifically, the project will use newly developed methods in non-equilibrium statistical mechanics and apply them to concepts from theoretical computer science. The project will allow the PI to spend 4 months with the group of Prof. Massimiliano Esposito at the University of Luxembourg. There he will be able to acquire state of the art technical skills in stochastic thermodynamics, which will enable him to complete the project described here and to initiate a research programme aimed at establishing the physical limits of computing. The main outcome of the project will be a series of models that describe the physical limits to power consumption of computations.The topic of the project is of high societal relevance. Computing related activities now account for more than 10% of the total energy consumption globally. It is reasonable to assume that the absolute power consumption due to computation will grow exponentially in the future in line with economic growth. This is unsustainable and there needs to be a radical improvement of the efficiency of computers. Currently, such improvements are mainly driven by incremental hardware and software optimisations. In order to sustain an exponentially growing demand for computation, game changing new energy efficient technologies are required that operate at ultra-low power, close to the limit of what is possible. While this project will not directly lead to such new technologies, it will provide a deep understanding of the causes of energy dissipation in computation. As such it will underpin future engineering efforts aimed at finding solutions to the current energy crisis in computing. Importantly, the results of this project will also provide a benchmark to assess the energy efficiency of current hardware technologies against the theoretical optimum.