Aside from the massive parallelism available in modern FPGAs, there are other two other key reasons why reconfigurable hardware is finding a fit in neural network processing in both training and inference. …
There is no real middle ground when it comes to TensorFlow use cases. …
Scaling the performance of machine learning frameworks so they can train larger neural networks – or so the same training a lot faster – has meant that the hyperscalers of the world who are essentially creating this technology have had to rely on increasingly beefy compute nodes, these days almost universally augmented with GPUs. …
So you are a system architect, and you want to make the databases behind your applications run a lot faster. …
The fields where machine learning and neural networks can have positive impacts seem almost limitless. …
Google created quite a stir when it released architectural details and performance metrics for its homegrown Tensor Processing Unit (TPU) accelerator for machine learning algorithms last week. …
There has been much discussion about the “black box” problem of neural networks. …
If you can’t beat the largest cloud players at economies of scale, the only option is to try to outrun them in performance, capabilities, or price. …
Here at The Next Platform, we tend to focus on deep learning as it relates to hardware and systems versus algorithmic innovation, but at times, it is useful to look at the co-evolution of both code and machines over time to see what might be around the next corner. …
Spark has grown rapidly over the past several years to become a significant tool in the big data world. …
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