MN-Core

Custom deep learning processor

Overview

To speed up training of deep learning models, PFN is developing the MN-Core™ chip. It is a dedicated accelerator optimized for matrix computations needed for deep learning. MN-Core is expected to achieve a world class energy efficiency of 1 TFLOPS/W (half precision). By focusing on the functions needed for deep learning, the dedicated chip can boost effective performance in deep learning as well as reduce costs.

Features

Optimized for the training phase in deep learning

Extremely densely integrated matrix arithmetic units

In spring 2020, we’ll launch MN-3, a new large-scale cluster based on MN-Core.

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