A research team from Nvidia has provided interesting insight about using mixed precision on deep learning training across very large training sets and how performance and scalability are affected by working with a batch size of 32,000 using recurrent neural networks. …
There has been much written about the potential for FPGAs to take a leadership role in accelerating deep learning but in practice, the hurdles of getting from concept to high performance hardware design are still taller than many AI shops are willing to scale, particularly when GPUs dominate in training and in a pinch, standard CPUs will do just fine for datacenter inference since they involve little developer overhead. …
Enterprises that want to leverage the huge amounts of data they are generating to gain useful insights and make faster and better business decisions are going to have to use machine learning at scale for modeling and training. …
Despite some of the inherent complexities of using FPGAs for implementing deep neural networks, there is a strong efficiency case for using reprogrammable devices for both training and inference. …
FPGA maker Xilinx has acquired Chinese deep learning chip startup DeePhi Tech for an undisclosed sum. …
It has been difficult for the supercomputing community to watch the tools they have honed over many years get snatched up by a more commercially-oriented community like AI and popularized. …
Most companies in the HPC space have made the natural transition to moving into the deep learning and AI space over the last couple of years. …
Pathology laboratories are big data environments. However, these big data are often hidden behind expert humans who manually and with great care visually parse large complex and detailed datasets to provide critical diagnoses. …
When it comes to deep learning chip startups, hype moves fast but crossing the finish line to real production silicon takes an incredibly long time. …
As history reveals, building a thriving systems business in HPC is no simple task. …
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