Memory at the Core of New Deep Learning Research Chip
Over the last two years, there has been a push for novel architectures to feed the needs of machine learning and more specifically, deep neural networks. …
Over the last two years, there has been a push for novel architectures to feed the needs of machine learning and more specifically, deep neural networks. …
The trend at the high end, from supercomputer simulations to large-scale genomics studies, is to push heterogeneity and software complexity while reducing the overhead on the infrastructure side. …
Public clouds have proven useful to a growing number of organizations looking for ways to run their high-performance computing applications to scale without having to limit themselves to whatever computing capabilities they have in-house or to spending a lot of money to build up their infrastructure to meeting their growing needs. …
We are thrilled to announce the full-time addition of veteran IT journalist, Jeffrey Burt to The Next Platform ranks. …
Hardware and device makers are in a mad dash to create or acquire the perfect chip for performing deep learning training and inference. …
The race is on to carve a path to efficient extreme-scale machines in the next five years but existing processing approaches fall far short of the efficiency and performance targets required. …
High performance computing (HPC) is traditionally considered the domain of large, purpose built machines running some *nix operating system (predominantly Linux in recent years). …
With the current data science boom, many companies and organizations are stepping outside of their traditional business models to scope work that applies rigorous quantitative methodology and machine learning – areas of analysis previously in the realm of HPC organizations. …
Last week we discussed the projected momentum for FPGAs in the cloud with Deepak Singh, general manager of container and HPC projects at Amazon Web Services. …
Over the last couple of years, we have focused extensively on the hardware required for training deep neural networks and other machine learning algorithms. …
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