The role of FPGAs in accelerating AI workloads

March 25, 2023
FPGAs in accelerating AI

What is FPGA, and what are its features and advantages?

FPGA stands for field-programmable gate array, which is a type of programmable logic device that can be configured and optimized according to different AI algorithms and application requirements. Unlike other AI accelerators that have fixed hardware architectures, FPGA can offer a high degree of customizabilityflexibility, and scalability for AI workloads.

How FPGA works

FPGA works by using an array of logic blocks and interconnects that can be programmed to perform specific functions. Each logic block can implement simple logic operations, such as AND, OR, or XOR, or more complex operations, such as arithmetic units, memory units, or multiplexers. The interconnects can route the signals between the logic blocks and the input/output pins of the device. By changing the configuration of the logic blocks and interconnects, FPGA can implement different AI algorithms and applications.

FPGA can be reconfigured at runtime to adapt to different AI tasks, such as image recognition, natural language processing, or speech synthesis. This means that FPGA can switch between different hardware configurations without replacing the device or changing the system design. This also means that FPGA can update or modify the hardware configuration to accommodate the changes of AI algorithms and applications over time.

Why FPGA is suitable for AI inference

FPGA has the characteristics of low latencyhigh throughputlow power consumptionhigh reliability, etc., which are suitable for accelerating AI inference. AI inference is the process of applying a trained AI model to new data to generate predictions or decisions. For example, AI inference can be used to recognize faces in images, translate texts in different languages, or synthesize voices from texts.

  • FPGA can achieve low latency by directly ingesting data from sensors or cameras and processing it in parallel without memory transfers. Latency is the time delay between the input and output of a system. Low latency is important for AI inference applications that require real-time responses, such as autonomous vehicles or drones.
  • FPGA can achieve high throughput by exploiting the spatial parallelism and pipelining of AI workloads and using dedicated hardware units for arithmetic operations. Throughput is the amount of data that can be processed by a system per unit time. High throughput is important for AI inference applications that require high performance, such as smart cities or factories.
  • FPGA can achieve low power consumption by using dynamic voltage and frequency scaling and turning off unused logic blocks. Power consumption is the amount of energy that is consumed by a system per unit time. Low power consumption is important for AI inference applications that require energy efficiency, such as wearable devices or IoT devices.
  • FPGA can achieve high reliability by using error correction codes and fault tolerance techniques. Reliability is the ability of a system to perform correctly under various conditions. High reliability is important for AI inference applications that require safety and security, such as medical imaging or financial transactions.
Why FPGA is suitable for AI inference
Why FPGA is suitable for AI inference

How FPGA Accelerates AI Workloads and What are the Application Scenarios and Cases

  • FPGA can accelerate the data acquisition, preprocessing, computation, and output of AI workloads, improving the efficiency of the entire AI workflow.
    • FPGA can perform data acquisition by interfacing with various sensors and devices using standard or custom protocols.
    • FPGA can perform data preprocessing by applying filters, transformations, compression, encryption, or other operations to the raw data.
    • FPGA can perform computation by implementing various AI algorithms using hardware description languages or specialized software tools.
    • FPGA can perform output by sending the results to displays, speakers, actuators, or other devices using standard or custom interfaces.
  • FPGA can add AI capabilities to existing workloads in edge computing, industrial manufacturing, healthcare, financial security, and other fields, achieving intelligence and automation.
    • In edge computing, FPGA can enable real-time analysis and decision-making for applications such as smart cities, autonomous vehicles, or drones.
    • In industrial manufacturing, FPGA can enable quality control, predictive maintenance, or process optimization for applications such as smart factories, robotics, or industrial IoT.
    • In healthcare, FPGA can enable diagnosis, treatment, or monitoring for applications such as medical imaging, wearable devices, or telemedicine.
    • In financial security, FPGA can enable fraud detection, risk management, or compliance for applications such as banking transactions, credit card payments, or blockchain verification.
wearable devices uses FPGA
wearable devices uses FPGA

What are the Advantages and Challenges of FPGA Compared to Other AI Hardware Platforms?

  • Compared to other AI hardware platforms such as GPUFPGA has higher customizabilityflexibility, and scalability, which can better adapt to the changes of AI algorithms and applications.
    • FPGA can offer higher customizability by allowing designers to tailor the hardware architecture to the specific AI workload characteristics and requirements.
    • FPGA can offer higher flexibility by allowing designers to update or modify the hardware configuration without replacing the device or changing the system design.
    • FPGA can offer higher scalability by allowing designers to add more logic blocks or interconnects to increase the compute capacity or bandwidth as needed.
  • However, compared to GPUFPGA also faces the challenges of high programming difficulty, long development cycle, and incomplete ecosystem. It needs to rely on specialized software tools and platforms to reduce the development threshold and cost.
    • FPGA has a high programming difficulty because it requires hardware design skills and knowledge of hardware description languages or specialized software tools.
    • FPGA has a long development cycle because it requires multiple steps of synthesis, placement, routing, verification, and testing before deploying the hardware configuration to the device.
    • FPGA has an incomplete ecosystem because it lacks standardized frameworks, libraries, models, or benchmarks for AI workloads compared with GPUs.