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Published 2 months, 1 week ago

Tech IBM Published by J. Doe

IBM teams with industry partners to bring energy-efficient AI hardware to hybrid cloud environments

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The ability to make advances in AI, in particular, will empower collaborative scientific communities and bring automation and virtually unlimited computing resources to every facet of the scientific process. With the help of AI and the computers that power it, scientists will be able to accelerate and scale scientific discovery at a pace never before seen.

Red Hat Joins AI Hardware Center to help bring IBM Digital AI Cores to Red Hat OpenShift

Through its involvement in the AI Hardware Center, Red Hat is collaborating with IBM to build compatibility between IBM Digital AI Cores and Red Hat OpenShift, the industry’s most comprehensive enterprise Kubernetes platform.

Red Hat is collaborating with IBM’s AI hardware development stream and working to enable AI hardware accelerator deployment across hybrid cloud infrastructure: multi-cloud, private cloud, on-premise and edge. The integration of accelerators based on IBM Digital AI Cores with Red Hat OpenShift enables the accelerators to be deployed and managed as part of a hybrid infrastructure.

Open Source Analog AI Cores

Information is moved back and forth between computation and memory units every time an operation is performed, creating a limitation called the von Neumann bottleneck. We are developing analog AI that could provide significant performance improvements and energy efficiency by combining compute and memory in a single device, significantly alleviating this bottleneck.

We’re now releasing our Analog Hardware Acceleration Kit as an open source Python toolkit that enables a larger community of developers to test the possibilities of using in-memory computing devices in the context of AI.

Expanding Partnerships Around the AI Hardware Center

The IBM Research AI Hardware Center’s goal is to be the nucleus of a new ecosystem of research and commercial partners collaborating with IBM to further accelerate the development of AI-optimized hardware innovations.

This includes efforts with Synopsys, a leader in electronic design automation software and emulation and prototyping solutions. Synopsys also develops IP blocks for use in the high-performance silicon chips and secure software applications driving advancements in AI.

Heterogenous Integration

AI requires a lot of interconnect bandwidth connectivity to take advantage of increases in computing power. IBM and NY Creates are investing in a new cleanroom facility on the campus of AI Center member, SUNY-Poly, in Albany, New York, that will focus on advanced packaging, also called “heterogeneous integration,” to improve memory proximity and interconnect capabilities. This work will also help ensure that, as our new compute cores are developed, the memory bandwidth is increased in tandem.

Original article

Jan 31, 2021 at 10:33

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