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Intel Spring Crest Achieves Nervana

August 27, 2019

Author: Linley Gwennap

Intel plans to deliver its first accelerator product for deep-learning training, the Nervana NNP-T, by the end of this year. As the brand indicates, it’s the first product based on technology originally developed by startup Nervana, which Intel acquired in mid-2016 for $400 million. At Hot Chips, the company revealed that the processor, code-named Spring Crest, employs 24 AI-optimized cores to deliver a peak rate of 119 trillion operations per second (TOPS) at 1.1GHz. These operations use the Bfloat16 format Google developed for neural-network training. Although the TOPS rating is similar to that of Nvidia’s fastest GPU, Intel expects greater utilization of its compute resources.

To help achieve this utilization, Spring Crest integrates 60MB of SRAM, keeping more data close to the compute units. As Figure 1 shows, it also copackages four stacks of HBM2 memory with the processor to provide large high-speed storage. The processor die itself is substantial, measuring 680mm2 in 16nm TSMC technology. Intel claims the typical operating power ranges from 150W to 250W but admits the TDP is more than 300W, including the HBM power. The company will sell Spring Crest accelerator cards in PCI Express (PCIe) and OAM form factors. They have 16 interchip links (ICLs) that can connect 32 or more cards for training enormous models.

Although Intel offers a tensor-core compiler and tool chain for developing low-level software, most users can bypass this complexity by working at the framework level. The company supplies drivers for the popular TensorFlow and PaddlePaddle frameworks and plans to add PyTorch capability. The drivers convert models developed in these frameworks to nGraph, an open-source graph compiler originally developed by Nervana. Intel developed OpenVino to support its other AI accelerators, but nGraph is incompatible with OpenVino.

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