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IBM Launches Telum, Its New AI Chip

IBM Launches Telum, Its New AI Chip

  • Our goal is to continue improving AI hardware compute efficiency by 2.5 times every year for a decade, achieving 1,000 times better performance by 2029.

IBM has announced its new chip, Telum – a new CPU chip that will allow IBM clients to leverage deep learning inference at scale. The new chip features a centralised design, which allows clients to leverage the full power of the AI processor for AI-specific workloads, making it ideal for financial services workloads like fraud detection, loan processing, clearing and settlement of trades, anti-money laundering, and risk analysis.

A Telum-based system is planned for the first half of 2022. “Our goal is to continue improving AI hardware compute efficiency by 2.5 times every year for a decade, achieving 1,000 times better performance by 2029,” said IBM in a press release.

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The chip contains eight processor cores, running with more than 5GHz clock frequency, optimised for the demands of enterprise-class workloads. The completely redesigned cache and chip-interconnection infrastructure provide 32MB cache per core. The chip also contains 22 billion transistors and 19 miles of wire on 17 metal layers.

According to a recent Federal Trade Commission report, consumers reported losing more than $3.3 billion to fraud in 2020, up from $1.8 billion in 2019. With Telum, financial institutions will be able to move from fraud detection to a fraud prevention posture, catching instances of fraud while the transaction is still ongoing.

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In traditional computing systems such as CPUs, calculations are performed by repeatedly transferring instructions and data between the memory and processor, but AI workloads have much higher computational requirements and operate on large quantities of data. Therefore, as you infuse AI into application workflows, it is critical to have a heterogeneous system consisting of CPU and AI cores tightly integrated on the same chip to support very low-latency AI inference. Telum achieves just the same.

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