FPGAs Focal Point for Efficient Neural Network Inference
Over the last couple of years, we have focused extensively on the hardware required for training deep neural networks and other machine learning algorithms. …
Over the last couple of years, we have focused extensively on the hardware required for training deep neural networks and other machine learning algorithms. …
Over the last two years, we have highlighted deep learning use cases in enterprise areas including genomics, large-scale business analytics, and beyond, but there are still many market areas that are still building a profile for where such approaches fit into existing workflows. …
While there are some sectors of the tech-driven economy that thrive on rapid adoption on new innovations, other areas become rooted in traditional approaches due to regulatory and other constraints. …
Machine learning is a rising star in the compute constellation, and for good reason. …
Over the last year in particular, we have documented the merger between high performance computing and deep learning and its various shared hardware and software ties. …
There is little doubt that 2017 will be a dense year for deep learning. …
In the course of this three-part series on the challenges and opportunities for enterprise machine learning, we have worked to define the landscape and ecosystem for these workloads in large-scale business settings and have taken an in-depth look at some of the roadblocks on the path to more mainstream machine learning applications. …
In part one of this series last week, we discussed the emerging ecosystem of machine learning applications and what promise those portend. …
Earlier this month, Samsung acquired Viv, the AI platform built by the creators of Siri that seeks to “open up the world of AI assistants to all developers. …
For a topic that generates so much interest, it is surprisingly difficult to find a concise definition of machine learning that satisfies everyone. …
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