As the global demand for machine learning devices continues to grow, many major market players in industries such as EDA (electronic design automation), graphics cards, gaming, and multimedia are investing in innovative high-speed computing processors. Although artificial intelligence is primarily based on software algorithms that mimic human thinking and ideas, hardware is also an important component. Field programmable gate arrays (FPGAs) and graphics processing units (GPUs) are the two main hardware solutions for most AI operations. According to a leading research group’s forecast, the global AI hardware market size was $10.41 billion in 2021 and is expected to reach $89.22 billion by 2030, with a compound annual growth rate of 26.96% from 2022 to 2030.
Overview of FPGAs and GPUs
An Overview of FPGAs
Hardware circuits with reprogrammable logic gates are called Field Programmable Gate Arrays (FPGAs). When the chip is in the field, users can design unique circuits by overlaying configurations. This contrasts with standard chips that cannot be reprogrammed. With FPGA chips, you can build anything from simple logic gates to multi-core chipsets. The use of FPGAs is very popular where internal circuits are essential and expected to change. FPGA applications cover ASIC prototype design, automotive, multimedia, consumer electronics, and more. Low-end, mid-range, or high-end FPGA configurations can be selected depending on application requirements. Lattice Semiconductor’s ECP3 and ECP5 series, Xilinx’s Artix-7/Kintex-7 series, and Intel’s Stratix series are some popular low-power and low-design density FPGA designs.
Logic blocks are built using lookup tables (LUTs) with limited inputs and basic memory (such as SRAM or flash) to store Boolean functions. Each LUT is linked to a multiplexer and a trigger register to support timing circuits. Similarly, many LUTs are available to create complex functions. Read our FPGA blog for more information about its architecture.
FPGAs are better suited for embedded applications and use less power than CPUs and GPUs. These circuits are not limited by designs like GPUs and can be used for customized data types. Additionally, the programmability of FPGAs makes it easier to modify them to address security issues.
Advantages of FPGAs
Energy-Efficient:
- With the help of FPGAs, designers can precisely adjust hardware to meet the requirements of application programs. With their low power capabilities, the overall power consumption of AI and ML applications can be minimized. This can extend the life of devices and reduce the overall cost of training.
Easy-Flexibility
- FPGAs provide programmable flexibility for processing AI/ML applications. A single block or an entire block can be programmed as needed.
Parallel Processing
- FPGAs excel at processing short batch phrases and reducing latency. Reducing latency refers to the ability of a computing system to respond with minimal delay. This is crucial in real-time data processing applications such as video surveillance, pre-and post-processing, and text recognition, where every microsecond counts. Because they operate in a bare-metal environment without an operating system, FPGAs and ASICs are faster than GPUs.
An Overview of GPUs
The initial purpose of Graphics Processing Units (GPUs) was to create computer graphics and virtual reality environments that rely on complex calculations and floating-point capabilities to render geometric objects. Without them, modern AI infrastructure would be incomplete and not suitable for deep learning processes.
Artificial Intelligence (AI) requires a large amount of data to study and learn in order to succeed. To run AI algorithms and move large amounts of data, a significant amount of computing power is needed. GPUs can perform these tasks because they were created to quickly process large amounts of data required for generating graphics and video. Their widespread use in machine learning and AI applications is partially due to their high computational capabilities.
GPUs can simultaneously process multiple calculations. Therefore, programs can be distributed for training, greatly accelerating machine learning activities. With GPUs, multiple low-resource kernels can be added without affecting performance or power consumption. There are various types of GPUs on the market, typically categorized as data center GPUs, consumer-grade GPUs, and enterprise-grade GPUs.
Advantages of GPUs
Memory Bandwidth
- GPUs have excellent memory bandwidth, which allows them to perform calculations quickly in deep learning applications. When training models on large datasets, GPUs consume less memory. With up to 750GB of memory bandwidth, they can truly accelerate the fast processing of AI algorithms.
Multi-Core:
- Typically, GPUs are composed of many processor clusters that can be combined together. This greatly enhances the processing power of the system, especially for AI applications with parallel data input, convolutional neural networks (CNN), and ML algorithm training.
Flexibility:
- Due to the parallel capabilities of GPUs, you can group them into clusters and allocate jobs among these clusters. Another option is to use a single GPU with a dedicated cluster to train a specific algorithm. GPUs with high data throughput can parallelize the same operation across many data points, enabling them to process large amounts of data at an unprecedented speed.
Dataset Size
- GPU is one of the best options for efficiently handling data sets with many data points greater than 100GB, which is required for model training in AI algorithms that are memory-intensive computations. Since parallel processing began, they have provided raw computing power needed for the efficient processing of either the same or unstructured data.
- The two main hardware choices for running AI applications are FPGA and GPU. Although GPUs can handle massive amounts of data required for AI and deep learning, they have limitations in terms of energy efficiency, heat issues, durability, and the ability to update applications with new AI algorithms. On the other hand, FPGAs offer significant advantages for neural network and ML applications, including ease of updating AI algorithms, availability, durability, and energy efficiency.
- In addition, significant progress has been made in creating software for FPGAs, making compilation and programming easier. To ensure the success of your AI application, you must investigate your hardware options. As they say, carefully weigh your choices before deciding on a course of action.
- Softnautics AI/ML experts have extensive expertise in creating efficient machine-learning solutions for various edge platforms, including CPUs, GPUs, TPUs, and neural network compilers. We also provide secure embedded systems development and FPGA design services by combining the best design practices and the appropriate technology stack. We help businesses build high-performance cloud and edge-based AI/ML solutions across various platforms, such as critical phrase/speech command detection, facial/gesture recognition, object/lane detection, people counting, and more.
