Custom deep learning processor
To speed up the training of deep learning modeles, PFN is developing the MN-Core™ chip. It is a dedicated accelerator optimized for the 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.
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.
PFN’s MN-3 Tops Green500 List of World’s Most Energy-Efficient Supercomputers for Second Time
PFN Achieves Six-Fold Increase in Computational Speed for Deep Learning Workload with New Compiler for MN-Core Processor