Accelerating AI with FPGAs
Learn how FPGAs revolutionize AI applications with custom hardware acceleration, low latency, and energy efficiency.
The Advantage of Using FPGAs for AI
Field-Programmable Gate Arrays (FPGAs) are a type of reconfigurable hardware device that allows users to customize their digital logic circuits to perform specific tasks. Hence, using FPGAs for AI applications offers a significant advantage by allowing users to tailor circuits to specific AI workloads, leading to enhanced performance, energy efficiency, and adaptability.
With horizontal AI capabilities across silicon, IP, and software, our FPGAs provide seamless solutions to customer challenges from edge to cloud. Silicon capabilities range from high-speed transceivers to flexible DSP blocks with AI precision support such as FP32, FP16, INT8, INT4, and on-chip memory. Software and IP capabilities include flexible automatic generation of inference IP for AI and integration of hard and soft IP blocks for transceivers, security, video, and signal processing using the Intel FPGA AI Suite and Quartus Prime Software.
Benefits of FPGA in AI
Customization and Reconfigurability
FPGAs' reprogrammable and reconfigurable nature allows developers to experiment, iterate, and implement custom hardware accelerators tailored to specific AI workloads without needing new hardware.
Parallel Processing
FPGAs excel at parallel processing, allowing them to perform multiple operations simultaneously. This is particularly beneficial for AI tasks that involve heavy parallelism, such as neural network computations in machine learning.
Energy Efficiency
FPGAs can provide high energy efficiency for certain AI workloads. By designing specialized hardware circuits optimized for specific tasks, FPGAs can achieve better performance per watt compared to general-purpose devices such as CPUs or GPUs.
Low Latency
FPGAs can be integrated into edge and IoT devices to perform on-device AI processing. This reduces the need to transmit large amounts of data to centralized servers, saving bandwidth and improving latency. In the data center or network edge, FPGAs can generate tokens at very low latency for large language models (LLM) or accelerate anomaly detection at high data rates.
AI Applications
Edge AI
FPGAs are especially suited for edge AI in a wide range of applications across industrial, medical, test and measurement, aerospace, defense, and automotive. Data at the edge can be diverse, ranging from images, vibration sensors, and radar data to pressure sensors. Diverse I/O protocols, low latency, low power, and long lifetime are additional FPGA advantages at the edge.
Network
The network facilitates data transfers and communication between edge devices, cloud services, and other interconnected components. FPGAs are specially equipped with the latest generation of high-speed I/O standards to facilitate usage in wireless and wireline networking. As networks begin to add intelligence for various emerging applications, such as anomaly detection, wireless channel estimation, and wireless decoder convergence, FPGAs can play an effective role.
Cloud/Data Center/High Performance Computing
FPGAs have been widely deployed in cloud and data center environments to accelerate a wide range of workloads, including databases, genomics, and networking. The intrinsic advantages of FPGAs help AI inference tasks, such as large language models, conversational AI, and recommendation systems. Other FPGA neural network applications include anomaly detection at very high data rates in NICs, financial fraud detection, and high-speed trading. In addition, the high energy efficiency of FPGAs helps mitigate cooling costs and supports the development of greener AI technologies.
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Explore Resources to Get You Started
Intel® FPGA AI Suite
Speed up your FPGA development for AI inference using frameworks such as TensorFlow or PyTorch and OpenVINO toolkit, while leveraging robust and proven FPGA development flows with the Intel Quartus Prime Software.
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Intel® Distribution of OpenVINO Toolkit
An open-source toolkit that makes it easier to write once and deploy anywhere.
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