Power-aware Computing for Multi-Agent systems

Vaenthan Thiruvarudchelvan, Terence Bossomaier

Research output: Book chapter/Published conference paperConference paperpeer-review

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Agent-based modelling is becoming a common technique for studying complex phenomenon in diverse fields including sociology, economics and biology. This technique is assisted by the continuing exponential and pervasive growth in computing power. In recent years, engineering limits on processor speed have spurred focus on multi-core processing as a means of continuing this growth. In order to harness this computing power, however, careful concurrent programming is required to develop multi-threaded applications. Furthermore, the power consumed by systems has come under increasing scrutiny from the standpoints of thermal capacity, energy availability, and sustainability. We investigated two schemes for reducing simulation power demands using The Game of Life as a representative simulation: a) event-driven computation and b) dynamic frequency scaling in multi-threaded rate-limited applications. Both schemes were found to significantly improve energy efficiency. Both event-driven and parallelized computation are central to the low power usage of biological computation compared to silicon.
Original languageEnglish
Title of host publication2011 IEEE Symposium on Artificial Life (ALIFE) Proceedings
Place of PublicationUnited States
PublisherIEEE, Institute of Electrical and Electronics Engineers
Number of pages8
ISBN (Electronic)9781612840635
Publication statusPublished - 2011
Event2011 IEEE Symposium on Artificial Life (ALIFE) - Paris, France, Paris, France
Duration: 11 Apr 201115 Apr 2011

Publication series

ISSN (Print)2160-6374


Conference2011 IEEE Symposium on Artificial Life (ALIFE)
OtherThe Third International IEEE Symposium on Artificial Life (IEEE ALIFE 2011) held for the first time in Europe, in the intellectual, cultural and scientific cauldron of Paris, brings together researchers working on the emerging areas of Artificial Life and Complex Adaptive Systems, aiming to understand and synthesize life-like systems and applying bio-inspired synthetic methods to other science/engineering disciplines, including Biology, Robotics, Social Sciences, among others.


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