LPool - Rack-scale servers to Revolutionize AI Datacenters for Fast, Sustainable, Cost-efficient Computing
Location: Germany
Source: EU Funding & Tenders Portal
Over the past decade, the on-device performance of CPUs, GPUs, and AI accelerators has improved 100x faster than off-device data movement. This increasingly causes chips to wait for data, lengthening the time to run a job. This data bottleneck thus leads to massive inefficiencies, raising the cost, energy consumption, and carbon footprint to run AI jobs by 57% for datacenter providers and their en
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Participants
Sponsoring Agency | Obfuscated Data |
Company | Obfuscated Data |
Status
Original status | ongoing |
Taiyo status | Obfuscated Data |
Taiyo last update | 00-00-0000 |
Available timestamps | 00-00-0000 |
Available timestamp type | Obfuscated Data |
Contact
Contact name | Obfuscated Data |
Phone | 0000000000 |
ObfuscatedData@email.com | |
Address | Obfuscated Data, Obfuscated data, obfuscated data, Obfuscated data |
Description
Description | Over the past decade, the on-device performance of CPUs, GPUs, and AI accelerators has improved 100x faster than off-device data movement. This increasingly causes chips to wait for data, lengthening the time to run a job. This data bottleneck thus leads to massive inefficiencies, raising the cost, energy consumption, and carbon footprint to run AI jobs by 57% for datacenter providers and their end-users. ExpectedIT's solution consists of two new proprietary chips: the Large Pool CPU (LP-CPU) chip, its matching Pool chip, and accompanying novel server packaging technology. ExpectedIT will license the intellectual property (IP) for these innovations to chip and datacenter server vendors, enabling the best use of each device's precious off-device data movement. By removing the bottleneck, ExpectedIT's new solution allows AI or other data-intensive applications to run up to 10x faster. By completing the jobs in less time and using fewer hardware resources, LPool will offer datacenters and their end-users up to 2x reduction in costs, energy consumption, and carbon emissions to run a specific job. Our LPool technology will thus significantly impact AI's scalable and sustainable growth for diverse sectors like healthcare diagnostic AI, greentech, enterprise generative AI, and much more. We have validated our technology through over 100K simulation runs of the Verilog code. Based on these promising results, we have attracted the interest of one of the world's leading chip vendors with whom we are in active discussions for them to license the IP for our technology. This project will allow ExpectedIT to complete the engineering of the full chip logic for the Pool chip, the LP-CPU, and server packaging. It will also enable us to double our team size to 30 to carry out these activities, including business developers, to accelerate our go-to-market. Our ambition is to reach 5 licensees and generate over €110 million in royalty revenues by 2031. |
Original sub-sector | Obfuscated |
Original Currency | USD |
Original budget | 000000000000000 |
Procurement method | Obfuscated Data |
Budget | 000000000000000 |
Location
Region | Obfuscated |
Country | Obfuscated |
State | Obfuscated Data |
County | Obfuscated |
Location | Obfuscated Data, Obfuscated data, obfuscated data, Obfuscated data |
Source
Source reliability | High |
Data quality score | 100% |
Source | Obfuscated Data |
URL | obfuscated_data,obfuscateddata.com |
More Details
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