The upsurge in data generation and its computing has raised the necessity for more power, storage, and speed. What we call as big data is extremely memory-hungry and power-sapping and to fetch this requirement, engineers have suggested an innovative method. Recently, electrical engineers at Northwestern University and therefore the University of Messina in Italy have developed a replacement magnetic storage device that would potentially support the surge of data-centric computing, which needs ever-increasing power, storage, and speed. supported antiferromagnetic (AFM) materials, the device is that the smallest of its kind ever demonstrated and operates with record-low electrical current to write down data.
The post features a preferred start date of autumn 2020, although there's some flexibility in the start date. The post is predicated in Egham, Surrey, where the school is situated during a leafy campus almost Windsor Great Park and within commuting distance of London.
Northwestern’s Pedram Khalili, who led the research, said, “The rise of massive data has enabled the emergence of AI (AI) within the cloud and jittery devices and is fundamentally transforming the computing, networking, and data storage industries. However, existing hardware cannot sustain the rapid climb of data-centric computing. Our technology potentially could solve this challenge.”
The research was published on 10 February within the journal Nature Electronics.
Khalili is a professor of electrical and computer engineering at Northwestern’s McCormick School of Engineering. He co-led the study with Giovanni Finocchio, a professor of EE at the University of Messina. The team also included Matthew Grayson, a professor of electrical and computer engineering definition at McCormick. Jiacheng Shi and Victor Lopez-Dominguez, who are both members of Khalili’s laboratory, served as co-first authors of the paper.
As noted by Northwestern’s report, although AI offers promise to enhance many areas of society, including health care systems, transportation, and security, it can only meet its potential if computing can support it. Ideally, AI needs all the simplest parts of today’s memory technologies: Something as fast as static random access memory (SRAM) and with a storage capacity almost like dynamic random access memory (DRAM) or Flash. On top of that, it also needs low power dissipation.
The post features a preferred start date of autumn 2020, although there's some flexibility in the start date. The post is predicated in Egham, Surrey, where the school is situated during a leafy campus almost Windsor Great Park and within commuting distance of London.
Northwestern’s Pedram Khalili, who led the research, said, “The rise of massive data has enabled the emergence of AI (AI) within the cloud and jittery devices and is fundamentally transforming the computing, networking, and data storage industries. However, existing hardware cannot sustain the rapid climb of data-centric computing. Our technology potentially could solve this challenge.”
The research was published on 10 February within the journal Nature Electronics.
Khalili is a professor of electrical and computer engineering at Northwestern’s McCormick School of Engineering. He co-led the study with Giovanni Finocchio, a professor of EE at the University of Messina. The team also included Matthew Grayson, a professor of electrical and computer engineering definition at McCormick. Jiacheng Shi and Victor Lopez-Dominguez, who are both members of Khalili’s laboratory, served as co-first authors of the paper.
As noted by Northwestern’s report, although AI offers promise to enhance many areas of society, including health care systems, transportation, and security, it can only meet its potential if computing can support it. Ideally, AI needs all the simplest parts of today’s memory technologies: Something as fast as static random access memory (SRAM) and with a storage capacity almost like dynamic random access memory (DRAM) or Flash. On top of that, it also needs low power dissipation.
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