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Making NVIDIA FPGA with Jetson: What You Need to know

NVIDIA is a heavyweight in the Graphics Processing Units (GPUs) market. The company’s primary focus is to develop a wide range of solutions for the GPU market, especially as they relate to solving some of the existing challenges in the aforementioned market.

Although NVIDIA doesn’t manufacture Field Programmable Gate Arrays (FPGAs), programmers and developers may be able to carve out something from the existing products. One of such ways is by using the Jetson platform. In this article, you are going to discover what difference the Jetson platform makes and every other thing you need to know about NVIDIA FPGA.

What is Jetson

It is a development kit that provides a wide range of solutions for both the embedded applications and autonomous machines markets. According to the manufacturer, these solutions are geared towards providing quick deployment of these solutions to the targeted markets or applications.

How to use the Jetson Platform for FPGA Design


The relationship between the Jetson platform and the NVIDIA FPGA is that the former may be tweaked for some Field Programmable Gate Array (FPGA) solutions. Shuracore hints that the FPGAs may be utilized for the capturing and processing of the several video streams and high-speed sensor data that are derived from the system.

However, one of the challenges to this is the inability to establish a connection between the Field Programmable Gate Arrays (FPGAs) and Graphics Processing Units (GPUs). The most feasible solution for this is the deployment of the GPUDirect RDMA technology to do away with the centralized communication of the Central Processing Unit (CPU).

Advantages of the NVIDIA Jetson

Here are some of the benefits that come with working with the Jetson development kit designed by NVIDIA.

1. Multi-Platform Compatibility

The development kit is compatible with most of the third-party platforms you may want to use for the configuration. From CUDA to Linux Operating System (OS), the NVIDIA Jetson kit supports it all.

2. Accelerated Designs

One of the core reasons why developers prioritize the NVIDIA Jetson is its accelerated design concepts. This enables the real-time designing and deployment of solutions across different facets.

Second and most important, the accelerated design uses a wide range of modules, which are small form-factor and high-performance computers. The combination of those help to bolster the performance of the targeted applications, especially with the integration of an ecosystem of sensors, services and SDKs.

Beating the Competition

The absence of NVIDIA FPGA means that other companies offering Field Programmable Gate Arrays (FPGAs) stand a better chance at beating it to the competition.

AMD, one of the leading manufacturers of Central Processing Units (CPUs) and Graphics Processing Units (GPUs) has been making moves to delve into newer markets. The FPGA market poses greater advantages to the company, hence the decision to acquire Xilinx in October 2020.

Xilinx, which is now a part of AMD, is the leading manufactures of Field Programmable Gate Arrays (FPGAs). The company has been in the business since the 1980s but would become a part of AMD in early 2020 after a record $49 billion deal.

Now, AMD has continued its push and has now targeted some of the new FPGA solutions at NVIDIA. According to a publication on HPC Wire, the duo of AMD-Xilinx has taken a swipe at NVIDIA by launching an improved version of the VCK5000 Interfacing Card.

The improved card is optimized to offer improved functionalities than what NVIDIA already offers via its Graphics Processing Units (GPU) line of products.

Here are some of the benefits that the AMD-Xilinx VCK5000 Interfacing Card has to offer over NVIDIA’s existing GPU products:

Increased Peak

The estimated peak of the AMD-Xilinx VCK5000 Interfacing Card is 90 TOPS. This favorably competes against the 34 to 42% peak TOPS of the NVIDIA GPUs.

Better Performance

The performance of AMD’s VCK5000 Interfacing Card can also beat that of NVIDIA, in terms of the Artificial Intelligence (AI) model workloads. It is estimated to deliver up to 2 times TCO over the one offered by NVIDIA’s T4. The Interfacing Card also beats the performance of the previous AI Interfacing Cards by moving up the ladder with up to 3x performance.


Full pcb manufacturing

With the Xilinx acquisition, AMD has stepped into the FPGA market. Now, the company is at one end of the rope while NVIDIA is at the other. While AMD manufacturers and controls a considerable market share in the FPGA industry, NVIDIA leads in the GPU market.

Let us look at some of the clear differences between the two product lines (FPGAs and GPUs) offered by the both industry-leaders.

Targeted Application for Deep Learning

NVIDIA plays an active role in the furtherance of deep learning by optimizing most of its Graphics Processing Units (GPUs) to fit into that purpose.

Since deep learning has to do with the parallel calculations of image recognition and other related AI workloads, GPUs tend to fit into use case perfectly.

However, the use of GPUs may not work in all AI workload scenarios. For example, GPUs may not be able to facilitate the overall speed expected of AI workloads. The ASICs fit more into that, because that is the primary designation.

This is where the use of a Field Programmable Gate Array (FPGA) becomes important. FPGAs can be used to customize the hardware of the AI to fit into the targeted use cases, as well as improving the speed of parallel calculations. Since FPGAs are customizable or configurable, it is therefore understandable why it becomes a natural replacement for both the ASIC and GPU for higher AI workloads.

Ease of Programming

Configuring or optimizing a Field Programmable Gate Array (FPGA) isn’t easy. It typically requires complex programming languages based on the Hardware Description Language (HDL). Not all programmers may be able to get the job done with this.

That is why you may want to prioritize working with a GPU, which uses some of the basic and popular programming languages. Examples of the supported programming languages are Python, C, Java and C++.


NVIDIA FPGA is nonexistent for now, but the company has been making huge investments in improving its GPU offerings.

Consider comparing the individual properties and market opportunities presented by both the FPGA and GPU before you decide on the one to go for.



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