Energy-Efficient Algorithms

Demaine, Erik D.; Lynch, Jayson; Mirano, Geronimo J.; Tyagi, Nirvan

doi:10.1145/2840728.2840756

Cited by 22 publications

(18 citation statements)

References 32 publications

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“…A recent study 21 performed a systematic study of the energy complexity of several classic algorithms exploring the time/space/energy trade-off in order to reduce their energy complexity by applying Landauer’s principle.…”

Section: Power Consumption and Easmentioning

confidence: 99%

Population size influence on the energy consumption of genetic programming

Álvarez

Castillo

Vega

et al. 2022

Measurement and Control

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Evolutionary Algorithms (EAs) are routinely applied to solve a large set of optimization problems. Traditionally, their performance in solving those problems is analyzed using the fitness quality and computing time, and the effect of evolutionary operators on both metrics is routinely used to compare different versions of EAs. Nevertheless, scientists face nowadays the challenge of considering the energy efficiency in addition to computational time, which requires studying the energy consumption of algorithms. This paper discusses the interest of introducing power consumption as a new metric to analyze the performance of standard genetic programming (GP). Two well-studied benchmark problems are addressed on three different computing platforms, and two different approaches to measure the power consumption have been tested. Analyzing the population size, the results demonstrates its influence on the energy consumed: a non-linear relationship was found between size and energy required to complete an experiment. This analysis was extended to the cache memory and results show an exponential growth in the number of cache misses as the population size increases, which affects the energy consumed. This study shows that not only computing time or solution quality must be analyzed, but also the energy required to find a solution. Summarizing, this paper shows that when GP is applied, specific considerations on how to select parameter values must be taken into account if the goal is to obtain solutions while searching for energy efficiency. Although the study has been performed using GP, we foresee that it could be similarly extended to EAs.

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Section: Power Consumption and Easmentioning

confidence: 99%

Population size influence on the energy consumption of genetic programming

Álvarez

Castillo

Vega

et al. 2022

Measurement and Control

View full text Add to dashboard Cite

show abstract

“…Standard ways of generating electricity, such as gas power plants, produce large amounts of greenhouse gases that contribute to global warming which threatens to change the Earth's climate in disastrous and irreversible ways in a matter of decades. Reducing energy use for deep learning and other AI applications should therefore be a general social concern, and research is investigating generally how to make computation more energy efficient (Demaine, Lynch, Mirano, and Tyagi 2016).…”

Section: Machine Learningmentioning

confidence: 99%

Energy Requirements Undermine Substrate Independence and Mind-Body Functionalism

Thagard

2022

Philos. sci.

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Substrate independence and mind-body functionalism claim that thinking does not depend on any particular kind of physical implementation. But real-world information processing depends on energy, and energy depends on material substrates. Biological evidence for these claims comes from ecology and neuroscience, while computational evidence comes from neuromorphic computing and deep learning. Attention to energy requirements undermines the use of substrate independence to support claims about the feasibility of artificial intelligence, the moral standing of robots, the possibility that we may be living in a computer simulation, the plausibility of transferring minds into computers, and the autonomy of psychology from neuroscience.

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“…Landauer [26], Bennett [27] and Lloyd [28] provide detailed discussions and bibliographies on this line of work. Demaine et al [29] extract a mathematical model from this line of work and analyze the energy complexity of various algorithms within this model. Note that the Thompson model we use is one informed by how modern VLSI circuits are created, even though they operate at energies far above ultimate physical limits.…”

Section: Other Energy Models Of Computationmentioning

confidence: 99%

Energy, Latency, and Reliability Tradeoffs in Coding Circuits

Blake

Kschischang

2019

IEEE Trans. Inform. Theory

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It is shown that fully-parallel encoding and decoding schemes with asymptotic block error probability that scales as O (f (n)) have Thompson energy that scales as Ω ln f (n)n . As well, it is shown that the number of clock cycles (denoted T (n)) required for any encoding or decoding scheme that reaches this bound must scale as T (n) ≥ ln f (n). Similar scaling results are extended to serialized computation. The Grover information-friction energy model is generalized to three dimensions and the optimal energy of encoding or decoding schemes with probability of block error Pe is shown to be at least Ω n (ln Pe (n)) 1 3 .. This approach is generalized to serial implementations. Recent work on the energy complexity of good decoding has focused largely on planar circuits. However, circuits implemented in three-dimensions exist [6], and so we generalize the recent information friction (or bit-meters) model introduced by Grover in [3] to circuits implemented in three-dimensions and extend the technique of Grover to show that, in terms of block length n, a bit-meters coding scheme in which block error probability is given by P e (n) has encoding/decoding energy that scales as Ω n (ln P e (n)) 1 3. We show how this approach can be generalized to an arbitrary number of dimensions. In Section II we discuss prior work, and in particular we discuss existing results on complexity lower bounds for different models of computation for different notions of "good" encoders and decoders. The main technical results of this work are in Section III, where we study the Thompson energy model, and in Section IV, where we study a multi-dimensional generalization of the Grover bit-meters model. In these sections we present lower bounds for decoders, as the derivation for encoding lower bounds is almost exactly the same. We provide an outline of the technique for encoder lower bounds in Section V. In Section VI we discuss limitations and weaknesses in the model used. In Section VII, we discuss other energy models of computation.

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Energy-Efficient Algorithms

Cited by 22 publications

References 32 publications

Population size influence on the energy consumption of genetic programming

Population size influence on the energy consumption of genetic programming

Energy Requirements Undermine Substrate Independence and Mind-Body Functionalism

Energy, Latency, and Reliability Tradeoffs in Coding Circuits

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