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“The whole message is, ‘Let’s not be bottlenecked by multiplication matrix and GPU memory,'” Chen said. “Ours may be the first algorithmic approach to beat GPU, but I hope it’s not the last. The field needs new ideas, and that is a big part of what MLSys is about.”
https://news.rice.edu/2020/03/02/deep-learning-rethink-overcomes-major-obstacle-in-ai-industry/
https://github.com/keroro824/HashingDeepLearning
https://arxiv.org/abs/1903.03129"
So he, Chen and Medini published the initial results on arXiv in March 2019 and uploaded their code to GitHub. A few weeks later, they were contacted by Intel.“Our collaborators from Intel recognized the caching problem,” he said. “They told us they could work with us to make it train even faster, and they were right. Our results improved by about 50% with their help.”
Shrivastava said SLIDE hasn’t yet come close to reaching its potential.
“We’ve just scratched the surface,” he said. “There’s a lot we can still do to optimize. We have not used vectorization, for example, or built-in accelerators in the CPU, like Intel Deep Learning Boost. There are a lot of other tricks we could still use to make this even faster.”
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