![]() ![]() FPGAs are often used where data must traverse many different networks at low latency. Due to their programmable I/O interface and highly flexible fabric, FPGAs are also well suited to the following tasks: Overcoming I/O bottlenecks. It’s possible to use a portion of an FPGA for a function, rather than the entire chip, allowing the FPGA to host multiple functions in parallel.ĪI and Deep Learning Applications on FPGAsįPGAs can offer performance advantages over GPUs when the application demands low latency and low batch sizes-for example, with speech recognition and other natural language processing workloads. FPGAs can also accommodate multiple functions, delivering more energy efficiency from the chip. Low power consumption: With FPGAs, designers can fine-tune the hardware to the application, helping meet power efficiency requirements. This characteristic makes them ideal for use in industrial defense, medical, and automotive markets. FPGAs have long product life cycles, so hardware designs based on FPGAs can have a long product life, measured in years or decades. By integrating additional capabilities onto the same chip, designers can save on cost and board space. Furthermore, FPGAs can be used for more than just AI. Excellent value and cost: FPGAs can be reprogrammed for different functionalities and data types, making them one of the most cost-effective hardware options available. Designers can build a neural network from the ground up and structure the FPGA to best suit the model. FPGAs offer several advantages for deep learning applications and other AI workloads: Great performance with high throughput and low latency: FPGAs can inherently provide low latency as well as deterministic latency for real-time applications like video streaming, transcription, and action recognition by directly ingesting video into the FPGA, bypassing a CPU. The reprogrammable, reconfigurable nature of an FPGA lends itself well to a rapidly evolving AI landscape, allowing designers to test algorithms quickly and get to market fast. GPUs don’t deliver as much performance as an ASIC, a chip purpose built for a given deep learning workload.įPGAs offer hardware customization with integrated AI and can be programmed to deliver behavior similar to a GPU or an ASIC. However, running AI on GPUs has its limits. In other words, they can deliver incredible acceleration in cases where the same workload must be performed many times in rapid succession. GPUs excel at parallel processing, performing a very large number of arithmetic operations in parallel. Because GPUs were specifically designed to render video and graphics, using them for machine learning and deep learning became popular. 1Įarly AI workloads, like image recognition, relied heavily on parallelism. By using FPGAs to accelerate search ranking, Bing realized a 50 percent increase in throughput. Five years later, Microsoft’s Bing search engine was using FPGAs in production, proving their value for deep learning applications. 1FPGAs offered a combination of speed, programmability, and flexibility-delivering performance without the cost and complexity of developing custom application-specific integrated circuits (ASICs). In 2010, Microsoft Research demonstrated one of the first use cases of AI on FPGAs as part of its efforts to accelerate web searches. The tech industry adopted FPGAs for machine learning and deep learning relatively recently. This capability makes FPGAs an excellent alternative to ASICs, which require a long development time-and a significant investment-to design and fabricate. Unlike graphics processing units (GPUs) or ASICs, the circuitry inside an FPGA chip is not hard etched-it can be reprogrammed as needed. Field programmable gate arrays (FPGAs) are integrated circuits with a programmable hardware fabric. ![]()
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