An FPGA, or Field Programmable Gate Array, is a general computing device. This chip is programmable, so it can perform any function or act as a particular chip. It can process all kinds of workloads, including graphics. In contrast, an FPGA GPU can execute specific instructions for a particular type of workload. Here’s what you should know before investing in an FPGA.
Compared to GPUs
CPUs and GPUs are the two main processors in today’s modern PCs. While the CPU remains a vital piece of hardware, GPUs are a much better option for graphics processing. GPUs can perform thousands of calculations at once, and they can even handle more complex tasks than CPUs. In addition, compared to CPUs, GPUs are much more efficient at lighting and rendering shapes. These differences make it clear why more computer systems are using GPUs today.
We can compare the performance of GPUs and TPUs directly by running a simple model using the tf_flowers dataset. We ran the same code on three different backends: GPUs (NVIDIA P100 with Intel Xeon 2GHz dual-core CPU, 13GB RAM) and TPUs (8-core TPUv3 with a quad-core CPU, and an accompanying tutorial notebook that demonstrates the best practices for optimizing TPU performance.
CPUs are expensive and rarely purchased in isolation. Rayming PCB & Assembly spend thousands of dollars on CPUs, while GPUs sell for a few hundred dollars. The only downside of GPUs is that they are not suited for all tasks. If you’re interested in running high-end games and graphics, a GPU is an excellent choice. A GPU can render complex 3D scenes with greater detail. And if you need to perform calculations on large datasets, a GPU is an excellent choice.
Professional manufacturers assemble professional GPUs, which means they have greater control over the process. In addition, they take care of ISV certification, which ensures that software vendors can support their products. Professional GPUs also run better in CAD applications and video editing.
Energy efficiency has become one of the defining characteristics of performance in recent years. However, as application and problem scale increase exponentially, energy consumption also rises. GPUs and conventional processors both have inherent limitations, but FPGAs offer a good compromise between programmability and energy efficiency. Moreover, they can suit the specific needs of a target application. As a result, they can scale up in size and improve performance while using less energy.
However, to match the performance of FPGA and GPU, one has to consider the power efficiency of the GPU. In contrast to an FPGA, the GPU is up to 56 GFLOP/W, more energy-efficient than an FPGA. One must have a 10x faster processor than an ASIC to match that. For example, the ultrascale+ HDL driver can achieve 700 MHz, while Spartan 6 can work well with 250 MHz.
In addition to this, an FPGA can implement more complex algorithms. For example, a stereo-BM kernel implemented in OpenCV uses CUDA. Moreover, the power consumption per frame for the FPGA implementation is 1.2 times lower than that of the GPU. Further, the GPU has a better memory access pattern. However, the GPU is not as good for solving complex problems involving data locality.
As the deep learning industry grows rapidly, GPUs and FPGAs must evolve to meet these demands. Currently, GPU vendors need to modify their current architectures to support different data types. As a result, users must postpone their projects until they develop a new architecture. In contrast, the re-configurability of FPGAs allows developers to implement custom data types independently.
The FPGA is a silicon-based semiconductor with many configurable logic blocks connected by programmable interconnects. Its design allows it to perform multiple functions at once. This enables it to perform higher-level calculations more quickly than a CPU. GPUs are suited for target applications where high-speed processing is critical. The following are some common uses of the GPU. They can speed up computer games and other tasks.
Embedded vision systems use an FPGA for several purposes. In gaming, for example, it can accelerate the processing of complex visual elements. It can also handle large-scale arithmetic operations. In addition to gaming, an FPGA can be helpful for IoT applications that need to analyze millions of data points simultaneously. It can even be beneficial for the processing of real-time diagnostic logic.
A significant benefit of the FPGA is its ability to work with thousands of memory units. In addition, an FPGA can also be configured with thousands of memory units, making it possible to perform massively parallel computations. Ultimately, FPGAs can match the performance of a GPU, so it is a good choice for embedded applications.
Because of their programmability, FPGAs are also ideal for embedded applications. In addition, they have lower power consumption than GPUs. And unlike their analog counterparts, FPGAs are not limited by architecture, which allows them to be more flexible for safety concerns. In recent years, AMD and NVIDIA have benefited greatly from the artificial intelligence interest.
Purchasing a GPU or FPGA chip is not free. Many companies charge a high price for GPU implementations. The reason for this is that FPGAs are much more energy-efficient than GPUs. Compared to CPUs, FPGAs consume only about one-third of the power consumed by the former. The price of a GPU or FPGA is also dependent on the host machine.
The cost of an FPGA versus a GPU depends on the type of application it will do. The high-end models are expensive, power-hungry, and unsuitable for some markets and critical systems. To help you make the right decision, the paper compares the key performance indicators of a 28nm GPU or FPGA and provides a trade-off analysis. The paper discusses the benefits and drawbacks of each technology, including power consumption, latency, and flexibility.
The cost of an FPGA is higher than that of a GPU. However, FPGAs have many advantages over GPUs. The FPGAs can be faster and more efficient, and their power consumption is much lower than that of a GPU. Both GPUs and FPGAs can work in parallel, with the Xilinx FPGA outperforming the latter for deep-learning tasks. In addition, GPUs require more complex compute resources, causing them to consume more power.
Process of designing an FPGA
The FPGA design flow is a complex procedure that includes many steps. It begins with an occupancy map computed from the LiDAR hits and combines the layers. Then the design team uses different functional blocks in Matlab to verify the behavior and syntax of the design. This early identification of errors speeds up the design process and decreases the cost of the final FPGA design. Then a simulation is performed to validate the design. Finally, the developers use timing and structural simulation methods to verify that the design is correct and efficient during this process.
Often, the CPU is unable to achieve the desired speed. This is due to the way it works. It constantly fetches and decodes instructions, meaning that running a thousand-instruction loop requires a million fetches. FPGAs can hard-wire instructions so that the data input will match the output rate. These benefits can significantly improve the performance of your GPU. Here is a breakdown of the FPGA design process.
Once you’ve completed the high-level design, you’ll need to translate the netlist into a netlist for the FPGA. Then, you can run the first simulation iteration to see how the design will work. Once you’ve done this, you can move on to the next step – implementation. The next step is to map the design to the FPGA vendor’s tools. Most of these implementation tools can read netlist formats.
The first step in designing an FPGA is to design the memory for the processor. You should also consider the performance of the FPGA compared to the bare-metal version. For example, the FPGA version has fewer errors, and the reference code has a better dynamic range.