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Tag: ASIC

AWS Leans on Custom Silicon for Processing Advantage

Dec 21, 2020 |

You may know Amazon Web Services as a leading provider of public cloud services. But it’s also investing substantial sums into designing its own line of custom chips that can save customers a lot of money compared to Intel X86 and Nvidia GPU processors, for general purpose, as well as analytical and AI workloads

According to Raj Pai, vice president of AWS EC2 product management, tells Datanami that AWS customers will see a 40% price-performance increase when they adopt the sixth-generation EC2 instance, which is powered by a custom-designed Graviton2 processor that’s equipped with 64-bit Arm Neoverse cores. Read more…

Deep Learning Has Hit a Wall, Intel’s Rao Says

Nov 13, 2019 |

The rapid growth in the size of neural networks is outpacing the ability of hardware to keep up, said Naveen Rao, vice president and general manager of Intel’s AI Products Group, at the company’s AI Summit yesterday. Read more…

Disruptive Economics and Democratization of Data

Sep 11, 2019 |

It seems like only a few years ago that managing data growth was a high-priority objective for most users. What happened? The short answer is the same thing that happened to oil. Read more…

A Wave of Purpose-Built AI Hardware Is Building

May 15, 2018 |

Google last week unveiled the third version of its Tensor Processing Unit (TPU), which is designed to accelerate deep learning workloads developed in its TensorFlow environment. But that’s just the start of a groundswell of new processors and processing architectures, including Wave Computing, which claims its soon-to-be-launched processor will dramatically lower the barrier of entry for running artificial intelligence workloads. Read more…

Inside Intel’s nGraph, a Universal Deep Learning Compiler

Apr 26, 2018 |

The advent of deep learning has spawned new life into the quest to create artificial intelligence. But along the way, we’ve become awash in technological complexity, forcing data scientists to juggle multiple deep learning frameworks and hardware platforms against increased demands for their work. Read more…

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