Microsoft Azure is the world's largest cloud investment in FPGAs. General-purpose processors, the performance of which isn't ideal for graphics and video processing. GPUs offer parallel processing capabilities, making it faster at image rendering than CPUs. They are also flexible and reconfigurable over time, to implement new logic.Ī popular choice for AI computations. They can't be reconfigured as your needs change.įPGAs, such as those available on Azure, provide performance close to ASICs. Because FPGAs are reconfigurable, you can stay current with the requirements of rapidly changing AI algorithms.Ĭustom circuits, such as Google's Tensor Processor Units (TPU), provide the highest efficiency. This flexibility makes it easier to accelerate the applications based on the most optimal numerical precision and memory model being used. You can reconfigure FPGAs for different types of machine learning models. Implementations of neural processing units don't require batching therefore the latency can be many times lower, compared to CPU and GPU processors. Batching can cause latency, because more data needs to be processed. Asynchronous requests (batching) aren't needed.
Compared to other chips, FPGAs provide a combination of programmability and performance.įPGAs make it possible to achieve low latency for real-time inference (or model scoring) requests.
The interconnects allow these blocks to be configured in various ways after manufacturing. What are FPGAs?įPGAs contain an array of programmable logic blocks, and a hierarchy of reconfigurable interconnects.
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In this article, you learn about FPGAs and how to deploy your ML models to an Azure FPGA using the hardware-accelerated models Python package from Azure Machine Learning.