Machine Learning with a Memristor Boost
On today’s podcast episode of “The Interview” with The Next Platform, we talk with computer architecture researcher Roman Kaplan about the role memristors might play in accelerating common machine learning algorithms including K-means. Kaplan and team have been looking at performance and efficiency gains by letting ReRAM pick up some of the data movement tab on traditional architectures.
Kaplan, a researcher at the Viterbi faculty of Electrical Engineering in Israel, along with his team, have produced some interesting benchmarks comparing K-means and K-nearest neighbor computations on CPU, GPU, FPGA, and most notably, the Automata Processor from Micron to their …
Machine Learning with a Memristor Boost was written by Nicole Hemsoth at The Next Platform.
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