Artificial Intelligence (AI) is seeing an explosion of new applications. These new applications have the potential to transform how we live, work, and play, driving new insights and providing more intuitive interactions with our smart devices.
FPGAs performs very well and can be applicable in applications such as AI, big data or machine learning which requires analyzing large amounts of data.
AI in Autonomous Vehicles
AI is also the main driver for autonomous vehicles, around which Google, Uber, automakers and others have expanded efforts in the past year.
And Amazon is seeking to put AI to work in the supermarket—testing a system without cash registers or lines, where consumers simply grab their products and go, and have a bill tallied by artificial intelligence. How cool will that be?!
We will see robots in the home and powering self-driving cars, but also in railway stations, hospitals and elsewhere in cities in the near future. This could include delivery robots or devices to help mobility for blind people.
FPGAs fit well with Artificial Intelligence, offering sensor fusion and unified tools and workflows. They can be used to create power efficient solutions to complicated analytics problems, with only the hardware needed for the problem. FPGAs are all about system performance and latency.
While FPGAs have been around for decades, it’s the latest versions of the devices which are picking up the AI load.
FPGA performs very well and can be applicable in applications such as AI, big data or machine learning which requires analyzing large amounts of data. The Intel Stratix 10 FPGA outperforms the GPU and in addition to performance, Intel FPGA are powerful because they are adaptable and make it easy to implement changes by reusing an existing chip which lets a team go from an idea to prototype in half the time it takes to build an ASIC.
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