FPGAs and their applications

FPGA Minutes to Become an Expert
Introduction
What is an FPGA? FPGAs and their applications Popular Vendors Development process When to Choose an FPGA over a GPU/CPU?
FPGA Knowledge
Synthesis Flows High Level Synthesis Vendor Specific Primitives
Xilinx FPGAs
Evaluation boards Minimum Design for KV260 Development workflow and tools Getting Started
Lattice FPGAs
Evaluation boards Development workflow and tools Getting Started
Simulation Tools
CocoTB
AI and Machine Learning
Introduction to AI Architectures and Frameworks Quantisation Pruning
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Introduction

FPGAs are widely used in industry and academia due to their flexibility, high performance, low latency, determinism and reconfigurability. One of the key aspects of using FPGAs is the capability of implementing custom architectures on them, allowing researchers to experiment with new disruptive ideas, architecture improvements, hardware acceleration, critical processing, radiation hardening, and low-power circuitry.

Applications

Unlike GPUs and CPUs, FPGAs can perform workloads with less energy, resulting in more battery life and power efficiency. It is possible to perform hardware acceleration using FPGAs under certain conditions, like numerical precision, latency and capabilities. For instance, GPUs and CPUs are better than FPGAs for high-numerical precisions. However, in terms of latency, FPGAs outperform the earlier two.

Recently, for AI and Computer Vision, FPGAs have been a good option given their low latency and the capability of handling multi-quantisation networks.

Concretely, some applications are:

• Signal Processing: FPGAs are used for real-time processing of signals in communication systems, including modulation, demodulation, encoding, and decoding.
• Accelerated Computing: FPGAs are employed to accelerate workloads such as machine learning, deep learning, and data analytics by offloading computationally intensive tasks from CPUs.
• Custom Processing: They enable custom processing architectures that can handle specific tasks more efficiently than general-purpose processors.
• Radar Systems: FPGAs are used in radar systems for real-time signal processing, including beamforming and target tracking.
• Advanced Driver Assistance Systems (ADAS): FPGAs are used for real-time image and sensor data processing in ADAS, enabling features such as lane departure warning, collision avoidance, and autonomous driving.
• Imaging Systems: FPGAs are used in medical imaging devices like MRI and CT scanners for high-speed image processing and real-time data analysis.
• Robotics: FPGAs are employed in robotic systems for precise control and real-time processing of sensor data.
• Control Systems: They are used in industrial control systems for tasks such as motor control, signal processing, and automation.
• Encryption/Decryption: FPGAs are used for high-speed cryptographic processing, providing secure data transmission and storage solutions.
• Network Security: They are employed in network security devices for real-time packet inspection and firewall implementation.

RidgeRun Services

RidgeRun has expertise in offloading processing algorithms using FPGAs, from Image Signal Processing to AI offloading. Our services include:

• Algorithm Acceleration using FPGAs.
• Image Signal Processing IP Cores.
• Linux Device Drivers.
• Low Power AI Acceleration using FPGAs.
• Accelerated C++ Applications.

And it includes much more. Contact us at https://www.ridgerun.com/contact.