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H2020Докторантска мрежа2020–2025

APROPOS · Approximate Computing for Power and Energy Optimisation

„Хоризонт 2020“ — Действия „Мария Склодовска-Кюри“

Период
2020-11-01 → 2025-04-30
Финансиране от ЕС
4 095 308 €
Участници
14
Схема
MSCA-ITN

Линиите свързват координатора с партньорите.

Накратко на български

Приблизителните изчисления (Approximate Computing) намаляват точността на данни при разпознаване на образи или обработка на сензори, когато перфектният резултат не е задължителен. Това помага за повишаване на енергийната ефективност, за да се спре прекомерният разход на електроенергия от цифровите системи.

Този кратък обзор е генериран от изкуствен интелект

Кратко обяснение, генерирано от езиков модел по текста на CORDIS. Оригиналът е по-долу.

Резултати накратко

Approximate Computing for Power and Energy Optimisation

The APROPOS project addresses a growing global concern: the unsustainable energy demands of computing. If left unchecked, by 2040, digital systems could consume more electricity than the planet can provide. This rising energy use, seen clearly in both data centers and mobile broadband networks, has significant implications for climate goals, costs, and long-term digital accessibility. APROPOS offers a novel solution through the use of Approximate Computing (AxC), a technique that intentionally reduces computational precision in areas where perfect accuracy is not necessary. By doing so, it improves energy efficiency by 10 to 50 times without compromising performance. This approach is particularly effective in real-world scenarios like sensor data processing, pattern recognition, and data mining, where "good enough" results are acceptable and expected. In pursuit of its broader goal to address energy efficiency challenges in future embedded and high-performance computing through disruptive methodologies, APROPOS successfully delivered on its core objectives: equipping researchers with the expertise to earn internationally recognized PhDs, fostering leadership and innovation mindsets through collaboration with SMEs and spin-offs, and enabling the practical application of research in both academic and non-academic sectors. APROPOS also promoted open research practices, strengthened communication and career development skills, supported diversity and inclusion, and helped the fellows make informed choices about academic versus industry careers. In short, APROPOS successfully trained a new generation of researchers in energy-efficient computing, with a strong emphasis on innovation, open science, and cross-sector and cross-border collaboration. Fellows gained experience in both academic and industrial settings, developed a broad set of transferable skills, and were exposed to diverse career paths and research environments across Europe. As a result, APROPOS has made a lasting impact on both the scientific community and broader society, offering scalable solutions to reduce digital energy consumption while empowering a skilled, future-ready workforce to carry this mission forward.

Текст от CORDIS, на английски · Данни: CORDIS, © Европейски съюз

Цел на проекта

The Approximate Computing for Power and Energy Optimisation ETN will train 15 ESRs to tackle the challenges of future embedded and high-performance computing energy efficiency by using disruptive methodologies. Following the current trend, by 2040 computers will need more electricity than the world energy resources can generate. On the communications side, energy consumption in mobile broadband networks is comparable to datacenters. To make things worse, Internet-of-Things will soon connect up to 50 billion devices through wireless networks to the cloud. APROPOS aims at decreasing energy consumption in both distributed computing and communications for cloud-based cyber-physical systems. We propose adaptive Approximate Computing to optimize energy-accuracy trade-offs. Luckily, in many parts of the global data acquisition, transfer, computation, and storage systems there exists the possibility to trade off accuracy to less power and less time consumed. As examples, numerous sensors are measuring noisy or inexact inputs; the algorithms processing the acquired signals can be stochastic; the applications using the data may be satisfied with an “acceptable” accuracy instead of exact and absolutely correct results; the system may be resilient against occasional errors; and a coarse classification may be enough for a data mining system. By introducing a new dimension, accuracy, to the design optimization, the energy efficiency can even be improved by a factor of 10x-50x. We will train the spearheads of the future generation to cope with the technologies, methodologies, and tools for successfully applying Approximate Computing to power and energy saving. The training, in this first ever ITN addressing approximate computing, is to a large extent done by researching energy-accuracy trade-offs on circuit, architecture, software, and system-level solutions, bringing together world leading experts from European organizations to train the ESR fellows.

Оригинален текст от CORDIS (на английски).

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Данни: CORDIS, © Европейски съюз