Field Programmable Gate Array (FPGA) is an integrated circuit that can be configured and customized after manufacturing. These chips are called “field programmable” because of this ability. They consist of programmable logic blocks that can be set up to perform a wide variety of functions or act as logic gates, providing the user with great flexibility in how the circuit operates.
Field programmable gate arrays (FPGAs) are semiconductor devices that consist of configurable logic blocks (CLBs) and programmable connections. These blocks can perform simple to complex operations and can include memory components such as flip-flops or memory blocks.
FPGAs are similar to programmable ROM chips but can accommodate more gates and can be reprogrammed, unlike ASICs, which are designed for specific tasks. They can be used to customize microprocessors for specific uses and are common in various industries, including wireless communications, data centers, automotive, medical, and aerospace. The reprogrammable nature of FPGAs allows for flexibility and design updates as needed.
FPGAs are used in different industries and have diverse areas of implementation. Some of its primary areas of use include:
FPGAs can play an important role in smart power grid technology by improving performance and scalability while keeping power consumption low. This is particularly useful in Transmission and Distribution (T&D) stations where efficient power grids are required for optimal operation.
Enhanced car experiences
Microsemi FPGAs allow original equipment manufacturers (OEMs) and suppliers to create new safety applications for vehicles, such as cruise control, blind spot warning, and collision avoidance. FPGAs also provide cybersecurity features such as information assurance, anti-tamper, hardware security, and reliability features such as error-correcting memory and low static power.
Aerospace and defense
Industrial manufacturers offer highly rugged, rad-tolerant, often aerospace-grade, FPGAs to meet the performance, reliability, and lifetime demands of harsh environments. FPGAs provide greater flexibility than traditional ASIC implementations and are particularly suitable for processing-intensive space systems.
Computer vision systems
In today’s world, computer vision systems are prevalent in many gadgets such as video surveillance cameras, robots, and other devices. It is often necessary to use an FPGA-based system to enable these tools to interact with people appropriately based on their location, surroundings, and facial recognition capabilities.
The Internet of Things and Big Data lead to an exponential increase in the amount of data being captured and processed. The use of deep learning techniques for parallel computation leads to the need for computational capacity with low latency, resilience, and security. Due to the high space costs, adding more servers cannot meet this demand. FPGAs are gaining acceptance in data centers due to their ability to speed up processing, flexibility in design, and hardware-based security against software vulnerabilities.
real time systems
FPGAs are used in real-time systems where response time is critical, since traditional CPUs have unpredictable response times, making it difficult to accurately predict when a trigger will fire.
Creating the circuit architecture is the first step, then a prototype is built and tested with an FPGA, allowing for debugging. Once the prototype works as expected, the ASIC project is developed. This approach saves time, because building an integrated circuit can be tedious and complex.
FPGA-based acceleration as a service
FPGA-based systems can perform complex tasks and process data more quickly than their virtual counterparts. While not everyone may be able to reprogram an FPGA for a specific task, cloud services make FPGA-based data processing more accessible to customers. Some cloud providers even offer a new service called Acceleration as a Service (AaaS), which gives customers access to FPGA accelerators.
With AaaS, one can use FPGAs to accelerate different types of workloads, such as:
- Machine learning models for training
- Dealing with big data
- Video stream analysis
- Perform financial calculations
- Database enhancement
Some FPGA manufacturers are already building cloud FPGAs to speed up AI workloads and other computing power-intensive applications. For example, Intel operates the Alibaba Cloud AaaS service known as F1 cases. The Acceleration Stack for Intel Xeon CPUs with FPGAs, also available to Alibaba Cloud users, provides two popular software development streams, RTL and OpenCL.
Another big company in the industry, Microsoft, is also competing to build an effective AI platform. their project, brainwave, advances FPGA technology to speed up deep neural network inference. Like Alibaba Cloud, they also use Intel’s Stratix 10 FPGA.
FPGA vs GPU for Deep Learning/Artificial Intelligence
GPUs excel at parallel processing by performing many computations simultaneously, providing significant speed-up in situations where the same workload must be executed in rapid succession. However, running AI on GPUs has its limitations. GPUs do not provide the same level of performance as ASIC cards, which are chips designed specifically for deep learning workloads.
FPGAs, on the other hand, offer hardware customization with built-in AI capabilities and can be programmed to mimic the behavior of a GPU or ASIC. Their reprogrammable and reconfigurable nature makes them suitable for a rapidly changing AI landscape, allowing for rapid testing of algorithms and a faster time to market. FPGAs offer several advantages for deep learning applications and other AI workloads:
- A pinch of cumin: Compared to a standard GPU, an FPGA has a larger memory bandwidth which allows it to process large amounts of data.
- Excellent value and cost effectiveness: FPGAs can be reprogrammed for various functions, making them one of the most cost-effective hardware options out there. Designers can save cost and board space by integrating additional capabilities into the same chip.
- Low energy consumption: With FPGAs, devices can be fine-tuned with the application, helping to meet energy efficiency requirements.
- Symmetry: One can use the FPGA part of a function rather than the entire chip, allowing it to host multiple functions in parallel.
- Embedding AI into workloads: With FPGAs, AI capabilities such as deep packet inspection or financial fraud detection can be added to existing workloads.
- Provide acceleration for high-performance computing (HPC) clusters: FPGAs can facilitate convergence between AI and HPC by acting as programmable accelerators for inference.
Disadvantages of using FPGAs
- Programming: While FPGAs offer a high degree of flexibility, they can be difficult to reprogram, and more experienced programmers are needed on the market.
- Implementation complexity: While the possibility of using FPGAs to accelerate deep learning is promising, few companies have attempted to implement it. For many AI solution developers, the traditional combination of GPUs and CPUs is a more manageable option.
- cost: The difficulty of circuit reprogramming and the lack of experienced programmers in the market make using FPGAs to accelerate AI-based applications an expensive solution. The cost of multiple circuit reprogramming can be quite high for small projects.
- Libraries shortage: Supports a limited number of ML FPGAs out of the box.