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H2020Докторантска мрежа2019–2024

MANIC · Materials for Neuromorphic Circuits

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

Период
2019-11-01 → 2024-04-30
Финансиране от ЕС
4 128 381 €
Участници
12
Схема
MSCA-ITN

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Този кратък обзор е генериран от изкуствен интелект

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

Резултати накратко

Materials for Neuromorphic Circuits

Nowadays, it is impossible to envision a major industrial, scientific or societal step forward without the role of advanced computing technology, including supercomputers. However, computer technology is demanding an increasing amount of energy. For example, today’s supercomputers consume more than 10 MW each which is equivalent to the electricity consumption of 30,000 households. Moreover, this number will only grow with the increasing developments of the Internet-of-things which requires a massive increase in computing device to enable front-end sensory systems to extract information from its collected data. Therefore, the European Union, its international competitors (USA, Asia) and high-tech companies, like IBM, are investing considerable efforts into developing computing platforms, so-called neuromorphic computing, that will be able to emulate the human brain. Our brain can make more calculations per second than the fastest of supercomputers because of parallel processing: networks of neurons and synapses that are working simultaneously. And all this at a much lower power consumption of only 20 W, which is a fraction of the power needed by a supercomputer. To this end, there are efforts to emulate brain functions through novel architectures based on CMOS technology (silicon chips). The success of this approach has been made clear with the recent development of neuromorphic processors (TrueNorth by IBM, SpiNNaker and BrainScaleS by the HBP consortium or Loihi by Intel). The approach of MANIC is to look into alternatives to silicon in order to develop basic device units that are more fitting to the needs of cognitive-type processing than current transistors: the so-called memristive devices based on novel resistive switching materials. In the way to CMOS-free architectures, it is also crucial to be able to combine CMOS circuits with memristive devices. These new materials will become the key elements in a new generation of electronic devices that will be able to adapt to software and enable efficient learning.

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

Цел на проекта

Large efforts are invested into developing computing platforms that will be able to emulate the low power consumption, flexibility of connectivity or programming efficiency of the human brain. The most common approach so far is based on a feedback loop that includes neuroscientists, computer scientists and circuit engineers. Recent successes in this direction motivate the scientific community to start working on the next big challenge: using materials that emulate neural networks. For that, new players are needed: material scientists, who look into alternatives to silicon in order to develop basic device units, more fitting to the needs of cognitive-type processing than current transistors. We notice that recent progress in chemistry and materials sciences (atomically controlled materials) and nanotechnology (diversity of tools to probe the nanometer scale) brings exciting possibilities for novel approaches in the area of neuromorphic computing. Clearly, the type of materials, physical responses and spatial dimensions considered in the design of neuromorphic systems will crucially determine their utilization, properties and cost, and consequently their societal and economic impact. Therefore, it is urgent that chemists and materials scientists also join forces in the development of the future neuromorphic computer. MANIC aims to offer complementary expertise to current approaches by recruiting fifteen Early Stage Researchers (ESRs) and providing them with the best possible research, academic and professional training, to prepare them for the challenge of developing advanced materials with memory, plasticity and self-organization that will perform better than the current solutions to emulate neural networks and, eventually, learn.

Оригинален текст от CORDIS (на английски).

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Данни: CORDIS, © Европейски съюз