Field-Programmable Gate Arrays (FPGAs) are versatile and powerful devices used in various fields of electronics and computing. Unlike traditional processors (like CPUs or GPUs), which have fixed hardware circuits designed for specific tasks, FPGAs are reprogrammable silicon chips. This means their functionality can be customized to meet specific needs, even after they have been manufactured.
Why Are FPGAs Special?
FPGAs outstand the traditional processing circuits like CPUs and GPUs in some fields, mainly because:
Reconfigurability: The most notable feature of FPGAs is that they can be reprogrammed to perform different tasks. This makes them highly adaptable for various applications, from prototyping new hardware designs to implementing custom digital circuits. You can even implement your own CPU or GPU on them.
Parallelism: FPGAs can perform multiple operations simultaneously. This parallel processing capability allows them to handle complex computations much faster than conventional processors in some cases.
Customization: FPGAs can be tailored to specific applications, enabling optimized performance for tasks such as data processing, signal processing, and real-time computing.
These characteristics make FPGAs special for many purposes. For instance, reconfigurability allows the implementation of certain circuits that can be changed on the fly, parallelism allows low-latency and high-performance software acceleration, and customization opens opportunities for data processing.
At RidgeRun, our customers use them for low-latency camera capture, signal processing acceleration, and low-latency AI inference.
How Do FPGAs Work?
At the heart of an FPGA are programmable logic blocks and a reconfigurable interconnect. These elements configure the logic in each block and connect them together using the interconnects. It is like configuring logical gates and connecting wires. Here’s a simplified breakdown:
Logic Blocks: These are the basic building units that can be programmed to perform simple logic functions like AND, OR, NOT, etc. More complex functions can be built by combining these basic logic blocks.
Interconnects: These are the pathways that connect the logic blocks. The configuration of these pathways determines how the logic blocks communicate with each other. They emulate wires in the real world.
Input/Output Blocks (IOBs): These manage the data entering and leaving the FPGA, interfacing with other hardware components.
There are more blocks, depending on how complex the FPGA is. For instance, AMD, Altera and Lattice FPGAs have RAM blocks that work as volatile memories to store data, MAC blocks (Multiplier + Adder) to perform arithmetic, and hard IP cores to interact with standard protocols like SPI, I2C, PCIe, Network, etc.
Why Do We Want To Use Them?
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.
How Do We Get Started?
We are covering this and more topics in the following posts. Stay tuned, and don't miss them!
RidgeRun Is Here To Help You Reach The Next Level
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 using AMD XRT.
And it includes much more. Contact us at https://www.ridgerun.com/contact.