1. Model Development: The first step is to develop and train AI models using popular frameworks such as TensorFlow, PyTorch, or Caffe. These frameworks provide the tools needed to create sophisticated AI models tailored to specific applications.
2. Model Optimisation: Once the AI model is developed, it needs to be optimized for FPGA implementation. Tools such as OpenVINO, TensorFlow Lite, or Caffe2 can be used to streamline the model, ensuring it runs efficiently on the FPGA hardware.
3. FPGA Design: The next step involves designing the FPGA architecture using hardware description languages (HDLs) such as VHDL or Verilog. This step is critical for mapping the AI model onto the FPGA hardware, ensuring that it can leverage the parallel processing capabilities of the FPGA.
4. FPGA Programming: After the design is complete, the FPGA needs to be programmed using software development kits (SDKs) such as Xilinx Vivado or Intel Quartus. These tools help translate the HDL design into a format that can be loaded onto the FPGA.
5. Testing and Validation: The final step is to test and validate the FPGA implementation. This can be done using simulation, emulation, or prototyping tools to ensure that the AI model runs correctly and efficiently on the FPGA hardware.
By following these steps, developers can harness the power of FPGAs to accelerate AI models, providing high performance and energy efficiency for a wide range of AI applications.
AI & FPGA
FPGA for Artificial Intelligence (AI)
FPGA and AI open up new extensive opportunities in engineering. FPGAs provide adaptable, high-performance solutions that exceed traditional GPUs and CPUs while increasingly more complex AI-related technologies help to accelerate workloads in machine learning applications through AI acceleration.
Employing FPGA for AI-based projects, we can tailor your solutions to ever-changing environments. Unlike traditional processors, FPGAs ensure flexibility, continuous parallel computing functionality, and an impressive balance between performance and power consumption.
What is FPGA?
A Field-Programmable Gate Array (FPGA) is an integrated circuit that can be programmed and reprogrammed after manufacturing. Unlike traditional processors, which have fixed functionalities, FPGAs offer a high degree of flexibility, allowing them to be tailored to specific tasks. This adaptability makes FPGAs particularly valuable in the realm of artificial intelligence (AI), where they can be optimised to handle complex workloads such as deep learning and neural networks.
In AI applications, FPGAs can be configured to accelerate specific AI tasks, providing a balance between performance and power consumption that is often superior to traditional CPUs and GPUs. This capability is crucial for implementing advanced AI models that require significant processing power and efficiency. By leveraging the reprogrammable nature of FPGAs, developers can continuously update and optimise their AI systems to meet evolving demands.
Advantages of FPGAs in AI
Flexibility
Using FPGA for AI applications allows us to build adaptable solutions. Due to its hardware-defined nature, it minimises the wastage of processing time for fetching data.
Custom parallelism
FPGAs are designed to perform multiple parallel operations, making them ideal for managing large networks and boosting overall system performance.
Scalability
The programmable architecture allows adding an almost unlimited amount of FPGAs per slot in case the AI algorithm or AI training demands are more significant than a single device can handle.
Power and energy efficiency
FPGAs offer excellent performance with lower power consumption. They are resilient and can work in harsh conditions for extended durations.
Reduced latency
Processing large data sets in real-time is possible thanks to robust on-chip memory that helps minimise delays.
Minimal access time
High memory bandwidth ensures rapid processing of large data amounts, significantly reducing waiting times.
Implementing AI on FPGAs
Implementing AI on FPGAs requires a deep understanding of both AI algorithms and FPGA architecture. The process typically involves several steps, each of which is crucial for ensuring that the AI models run efficiently on the FPGA hardware.
Steps and Tools
Application Cases of FPGA-Based AI Solutions
Image transcoding & broadcasting
Real-time UHD video processing, AI-powered TV show categorisation for DTV platforms, audio-over-IP (AoIP) for sound reinforcement in audio systems, AR and VR, and real-time digital character PoC.
Next-gen test & measurement solutions
Test and measurement platforms, real-time instrumentation, high-end consumer electronics with immersive displays, intelligent transport, and embedded real-time analytics.
Edge AI automotive solutions
In-vehicle infotainment (IVI), advanced driver-assistance systems (ADAS), automated driving (AD), driver information systems, motor control algorithms, and other fault-tolerant solutions according to ISO-26262.
Industrial solutions
Intelligent vision-guided robotics, high-precision motor control algorithms, data centre acceleration, smart grid, smart factory, IoT gateways and appliances, human-machine interfaces (HMIs), on-board electronics for railway systems, 3D printing.
Smart solutions for MedTech
Genome research, hardware acceleration and data processing in molecular breeding and pharmacy, medical imaging with ultrasound, robot-assisted surgery, multi-parameter patient monitors and ECGs, diagnostics and clinical equipment.
Custom design services
Real-time rendering, FPGA in AI-enhanced video processing, automated image recognition and classification, image stitching, embedded vision systems, face detection and tracking, hand gesture and speed sign detection, object counting, and smart home.
FPGAs in AI Systems and Network Architectures
FPGAs play a crucial role in AI systems and network architectures, particularly in edge AI and real-time processing applications. Their unique combination of flexibility, performance, and low power consumption makes them ideal for these demanding environments.
Edge AI and Real-Time Processing
Edge AI refers to the deployment of AI models at the edge of the network, closer to the data source. This approach reduces the need for data to travel to centralised servers, enabling faster and more efficient processing. FPGAs are well-suited for edge AI applications due to their low power consumption, high performance, and flexibility.
In edge AI applications, FPGAs can be used to accelerate AI workloads such as computer vision, natural language processing, and predictive analytics in real-time. This capability is particularly important in applications like Advanced Driver Assistance Systems (ADAS), where low latency and high accuracy are essential.
FPGAs provide lower latency and higher accuracy compared to traditional CPUs and GPUs, making them an attractive choice for edge AI and real-time processing applications.
By leveraging FPGAs, developers can create AI systems that are not only powerful and efficient but also capable of adapting to the specific needs of their applications. This adaptability is key to meeting the growing demands of AI and ensuring that systems can continue to evolve and improve over time.
Our Projects
If case you are looking for a team of professionals to support your FPGA board design or traditional approach for IP-core development in Verilog/VHDL, explore our FPGA services.
Our Technology Map
Specialized tools
Vitis/Vivado, Quartus Prime, Diamond, Libero, Matlab
Software platforms
NVidia Jetson, Alveo, OpenVINO, TensorFlow, Keras, Caffe
Tools & Languages
Verilog, VHDL, VivadoHLS, Simulink/HDL Coder, С/C++, Python
Hardware design
High-speed PCBs, DDR4, JESD204b, HDMI, SDI, SI, PI, Thermo modeling
Platforms
Zynq US+, RFSoC, Cyclone10, ECP5, MPF500
Transceivers
AD9361, AD9371, ADRV9009, Radars, Custom AFE, Antenas
Network protocols
DPDK, UDP 10G, TCP 10G, TAPs, L1/L2 IP cores
Networking
1G, 10G, 25G/40G, 100G