Embedded FPGAs

 

Embedded FPGAs

 

Embedded systems can be configured with FPGAs providing organizations with systems capable of accelerating their AI workloads at the edge, processing and analyzing data in real-time.FPGAs are desirable because of their ability to accelerate AI workloads. This is so because they can be configured and programmed to deliver performance similar to that offered by GPUs and ASICs. Since AI applications change rapidly, the reconfigurable and reprogrammable nature of FPGAs makes them the ideal solution because they are capable of evolving along with the ever-evolving AI landscape. That is, they allow designers to quickly test their new algorithms and bring them to market as quickly as possible. 

 

 

Furthermore, FPGAs are desirable for AI and deep learning applications because they provide a variety of benefits. Here are some of those benefits: 

 

FPGAs eliminate memory buffering and overcome I/O bottlenecks, which often limit the performance of AI systems. That is, FPGAs accelerate data ingestion, speeding up an entire AI workflow. This is so because the ability to ingest data quickly significantly decreases the amount of latency, which is a requirement for systems that are used to perform mission-critical applications that require real-time analysis and decision making.  

 FPGAs are excellent for AI workloads where data is gathered from multiple sensors and/or cameras. For example, they are great for autonomous vehicles where data is fed to the system from various sensors, cameras, LiDAR, and audio sensors because of FPGAs’ ability to handle multiple data inputs. Additionally, some research has shown that FPGAs are extremely power efficient than GPUs for performing AI inference analysis. This is so because the logic offered by FPGAs is extremely efficient at executing applications, which offers higher performance per watt. This is so because FPGAs are capable of performing the same function that a CPU can perform by executing thousands of cycles in just a few cycles. 

 

Biggest benefit of an AI inference computer equipped with an FPGA is the ability of the FPGA to re-programmed and reconfigured for each specific application. The reconfigurability allows organizations to deploy FPGAs inference PCs to perform the latest deep learning inference innovations as they emerge.

  

FPGAs are a great option for accelerating inference analysis performed at the edge because they are extremely fast, extremely flexible, and they are very power efficient, making them great for deployment at the edge. Some organizations are performing deep learning inference analysis using CPUs to host FPGAs that run the inference application. 

 

FPGAs are especially important for mission-critical applications, such as autonomous vehicles and factory automation, because such applications require ultra-low latency analysis and decision making where every millisecond counts.  Furthermore, FPGAs provide highly customizable I/O, which is extremely important for inference analysis because it allows FPGAs to integrate with numerous sensors, cameras, and other devices that provide it with data.