By Cheng Wang, Flex Logix, Inc.
August 6th, 2018, eecatalog.com
Why the performance and flexibility offered by eFPGA is turning out to be a game changer for anyone designing AI and machine learning and struggling to meet the compute demands.
The market for artificial intelligence (AI) and machine learning applications has been growing substantially over the last several years. Designers have a tough row to hoe when it comes to satisfying these applications’ seemingly insatiable compute hunger. They are finding that traditional Von Neumann processor architectures are not optimal solutions for the neural networks fundamental to AI and machine learning.
When GPUs are used to train neural networks, they require floating pointing math that is very compute intensive. However, using integer math for inference, designers can speed computation by turning to FPGAs for neural network processing. Many companies are starting to recognize this, with Microsoft’s Project Brainwave, which uses FPGA chips to accelerate AI, as a perfect example.
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