20,103 research outputs found
Energy challenges for ICT
The energy consumption from the expanding use of information and communications technology (ICT) is unsustainable with present drivers, and it will impact heavily on the future climate change. However, ICT devices have the potential to contribute signi - cantly to the reduction of CO2 emission and enhance resource e ciency in other sectors, e.g., transportation (through intelligent transportation and advanced driver assistance systems and self-driving vehicles), heating (through smart building control), and manu- facturing (through digital automation based on smart autonomous sensors). To address the energy sustainability of ICT and capture the full potential of ICT in resource e - ciency, a multidisciplinary ICT-energy community needs to be brought together cover- ing devices, microarchitectures, ultra large-scale integration (ULSI), high-performance computing (HPC), energy harvesting, energy storage, system design, embedded sys- tems, e cient electronics, static analysis, and computation. In this chapter, we introduce challenges and opportunities in this emerging eld and a common framework to strive towards energy-sustainable ICT
Breaking On-device Training Memory Wall: A Systematic Survey
On-device training has become an increasingly popular approach to machine
learning, enabling models to be trained directly on mobile and edge devices.
However, a major challenge in this area is the limited memory available on
these devices, which can severely restrict the size and complexity of the
models that can be trained. In this systematic survey, we aim to explore the
current state-of-the-art techniques for breaking on-device training memory
walls, focusing on methods that can enable larger and more complex models to be
trained on resource-constrained devices.
Specifically, we first analyze the key factors that contribute to the
phenomenon of memory walls encountered during on-device training. Then, we
present a comprehensive literature review of on-device training, which
addresses the issue of memory limitations. Finally, we summarize on-device
training and highlight the open problems for future research.
By providing a comprehensive overview of these techniques and their
effectiveness in breaking memory walls, we hope to help researchers and
practitioners in this field navigate the rapidly evolving landscape of
on-device training.Comment: 8 pages, 3 figure
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