Summary
There exists a class of important computational problems that conventional computers are unable to address with reasonable efficiency. Such Combinatorial Optimization (CO) problems are pervasive in a wide range of critically important sectors of society, e.g. in business operations, manufacturing, and research, including man-power scheduling, vehicle routing, IC circuit layout, protein folding and DNA sequencing, efficient big-data clustering, election modelling, network diagnosis, modelling molecular dynamics, discovery of new medicines/chemicals/materials, and so forth. At present, the CO market size is of the order of €1B and is expected to exhibit a 56% market growth rate with a 2030 market size forecast at €50B. Since conventional computers are ineffective in handling large CO problems, dedicated hardware – both quantum and quantum-inspired – are intensely researched and developed world-wide. A quantum-inspired alternative – so-called Ising Machines – has been developed over the last 20 years by D-wave in superconducting technology and is commercially available. However, being a superconducting technology, it suffers from operation at 20 mK requiring 25 kW of cooling power, little hope for much miniaturization of the complete system (today about 30 m3), and extreme cost (~10 M$ per system). Clearly, there is a need for a fast, scalable, energy-efficient, and affordable Ising Machine in dedicated hardware, without any of the severe drawbacks of the D-wave technology. This is the problem that SPINTOP wants to address. SPINTOP will deliver fast, scalable, energy-efficient and affordable Ising Machines based on networks of spin Hall nano-oscillators (SHNOs) pioneered by the applicant (JÅ) in his ongoing ERC Adv. Grant TOPSPIN. The proposed technology has tremendous breakthrough innovation potential and will aid in solving societal challenges related to almost all UN Sustainable Development Goals.
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| Web resources: | https://cordis.europa.eu/project/id/101069424 |
| Start date: | 01-04-2022 |
| End date: | 30-09-2023 |
| Total budget - Public funding: | - 150 000,00 Euro |
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Original description
There exists a class of important computational problems that conventional computers are unable to address with reasonable efficiency. Such Combinatorial Optimization (CO) problems are pervasive in a wide range of critically important sectors of society, e.g. in business operations, manufacturing, and research, including man-power scheduling, vehicle routing, IC circuit layout, protein folding and DNA sequencing, efficient big-data clustering, election modelling, network diagnosis, modelling molecular dynamics, discovery of new medicines/chemicals/materials, and so forth. At present, the CO market size is of the order of €1B and is expected to exhibit a 56% market growth rate with a 2030 market size forecast at €50B. Since conventional computers are ineffective in handling large CO problems, dedicated hardware – both quantum and quantum-inspired – are intensely researched and developed world-wide. A quantum-inspired alternative – so-called Ising Machines – has been developed over the last 20 years by D-wave in superconducting technology and is commercially available. However, being a superconducting technology, it suffers from operation at 20 mK requiring 25 kW of cooling power, little hope for much miniaturization of the complete system (today about 30 m3), and extreme cost (~10 M$ per system). Clearly, there is a need for a fast, scalable, energy-efficient, and affordable Ising Machine in dedicated hardware, without any of the severe drawbacks of the D-wave technology. This is the problem that SPINTOP wants to address. SPINTOP will deliver fast, scalable, energy-efficient and affordable Ising Machines based on networks of spin Hall nano-oscillators (SHNOs) pioneered by the applicant (JÅ) in his ongoing ERC Adv. Grant TOPSPIN. The proposed technology has tremendous breakthrough innovation potential and will aid in solving societal challenges related to almost all UN Sustainable Development Goals.Status
SIGNEDCall topic
ERC-2022-POC1Update Date
09-02-2023
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