Artificial intelligence: The time to act is now

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Artificial intelligence will soon change how we conduct our daily lives. Are companies prepared to capture value from the oncoming wave of innovation? Yes, they have a fine MRI machine and powerful software to generate the images. But that's where the machines bog down. The radiologist has to find and read the patient's file, examine the images, and make a determination. What if artificial intelligence (AI) could jump-start that process by enabling real-time and more accurate diagnoses or guidance, beyond what human eyes can see?


Artificial intelligence: The time to act is now

#artificialintelligence

Artificial intelligence will soon change how we conduct our daily lives. Are companies prepared to capture value from the oncoming wave of innovation?


Artificial intelligence: The time to act is now

#artificialintelligence

Artificial intelligence will soon change how we conduct our daily lives. Are companies prepared to capture value from the oncoming wave of innovation?


Re-Architecting SoCs for the AI Era

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The growth of artificial intelligence (AI) demands that semiconductor companies re-architect their system on chip (SoC) designs to provide more scalable levels of performance, flexibility, efficiency, and integration. From the edge to data centers, AI applications require a rethink of memory structures, the numbers and types of heterogeneous processors and hardware accelerators, and careful consideration of how the dataflow is enabled and managed between the various high-performance IP blocks. This paper will define AI, describe its applications, the problems it presents, and how designers can address those problems through new and holistic approaches to SoC and network on chip (NoC) design. It also describes challenges implementing AI functionality in automotive SoCs with ISO 26262 functional safety requirements.


Artificial Intelligence Gets A Boost With The Latest Generation Intel XEON Scalable Processors That Drives Inference At Scale

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They also demand increased flexibility with hardware that allows them to program with mainstream languages at a higher abstraction level along with libraries. The data science community is looking for a complete solution stack that abstracts away the hardware specifics, allowing them the ease to crunch parallel workloads more efficiently.