
Inferring The Future Of The FPGA, And Then Making It
Technologies often start out in one place and then find themselves in another. …
Technologies often start out in one place and then find themselves in another. …
Sometimes, if you stick around long enough in business, the market will come to you. …
FPGAs might not have carved out a niche in the deep learning training space the way some might have expected but the low power, high frequency needs of AI inference fit the curve of reprogrammable hardware quite well. …
The field programmable gate space is heating up with new use cases driven by everything from emerging network, IoT, and application acceleration trends. …
To keep their niche in computing, field programmable gate arrays not only need to stay on the cutting edge of chip manufacturing processes. …
In the U.S. it is easy to focus on our native hyperscale companies (Google, Amazon, Facebook, etc.) …
Custom accelerators for neural network training have garnered plenty of attention in the last couple of years, but without significant software footwork, many are still difficult to program and could leave efficiencies on the table. …
Around this time last year, we delved into a new FPGA-based architecture that targeted efficient, scalable machine learning inference from startup DeePhi Tech. …
We spend a lot of time contemplating what technologies will be deployed at the heart of servers, storage, and networks and thereby form the foundation of the next successive generations of platforms in the datacenter for running applications old and new. …
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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