Jetson Nano. Hidden categories: Articles with short description Short description matches Wikidata.

Nvidia Jetson is a series of embedded computing boards from Nvidia. The algorithm is based on artificial neural networks that use deep learning methods for efficiency. Since object detection and recognition does introduce some computational complexity, performing it in real-time on embedded platforms used to be difficult — that is, before platforms like Nvidia Tegra K1 and X1 emerged, bringing down the performance-per-watt for such applications to levels acceptable for packing it into a relatively small physical footprint. Jetson TX1. Edge AI Momentum. GeForce M 10 Series of embedded computing boards from Nvidia. Architecture of the setup The application is based on the OpenCV library, which supports camera configuration and frame capture, does the road sign recognition and prepares images for h encoding and streaming to the paired platform we are using Microsoft Surface for demos. To specify image processing pipeline for testing, we consider a basic camera application as a good example for benchmarking.

Retrieved That is with L4T on both systems, which means the A57 is running in bit mode in userspace. GeForce 2 4 MX. Views Read Edit View history. You can also perform various tests on images with different resolutions to see how the performance depends on image size, content and other parameters. Total processing time is calculated for values from gray rows of the table. Actions Allwinner Ax Exynos i. There was also a lot of change in caching on the bit version which in itself made bit a lot smarter with cache. Help Learn to edit Community portal Recent changes Upload file. Jetson Xavier NX.

Jetson Nano is a small, powerful computer for embedded AI systems and IoT that delivers the power of modern AI in a low-power platform. Jetson Module Lineup. We've released the software for GPU-based camera application on Github and it's available to download both binaries and source codes for gpu camera sample project. Atom Jaguar -based Puma -based Quark. The algorithm is based on artificial neural networks that use deep learning methods for efficiency. It can run multiple modern neural networks in parallel and process data from multiple high-resolution sensors—a requirement for full AI systems. Nvidia Quadro Quadro Plex. Intelligent machine OEMs and AI application developers create breakthrough products with Jetson in the fields of manufacturing, logistics, retail, service, agriculture, smart city, and healthcare and life sciences. Jetson Support Resources Detailed hardware design collateral, software samples and documentation, and an active Jetson developer community are here to help. GeForce 20

EDIT: Changed to fix an obvious wetware failure on my part wetware being my brain. Retrieved The Cortex-A57 can fetch, decode, and dispatch three instructions per clock cycle, and it executes instructions out of program order to improve throughput. Apache Hadoop Linaro. Download as PDF Printable version. Click to Expand. Namely the package qnx-V3Q The QNX operating system also available for the Jetson platform, though it is not widely announced. According to my tests and research about the A57 the X1 runs at 1. First, the Cortex-A57 is 64 bit versus the 32 bit Cortex-A

The setup includes signs of different shapes and sizes which are displayed on the external monitor. There are a wide variety of factors for the better performance of the Cortex-A57 over the Cortex-A I suspect the quad core A57 at 1. Jetson Xavier NX. Single-board computer and single-board microcontroller. Namely the package qnx-V3Q From Wikipedia, the free encyclopedia. Nvidia Jetson is a series of embedded computing boards from Nvidia.

GeForce M 10 Technical Specifications. Views Read Edit View history. In performance tests with nbench the K1 processor gets a better result. Get to market faster with software, hardware, and sensor products and services available from Jetson ecosystem and distribution partners. Here we publish performance benchmarks for available Jetson modules. New designs should use Jetson TX2 4GB to run neural networks with double the compute performance or double the power efficiency of Jetson TX1—at the same price. We've released the software for GPU-based camera application on Github and it's available to download both binaries and source codes for gpu camera sample project. To specify image processing pipeline for testing, we consider a basic camera application as a good example for benchmarking.

Hidden categories: Articles with short description Short description matches Wikidata. NV1 NV2. Here you can get the document with the benchmarks. Technical Specifications. Namely the package qnx-V3Q Some of them are system related Here are a few of the reasons, but certainly not all. Total processing time is calculated for values from gray rows of the table. Atom Jaguar -based Puma -based Quark. GeForce 2 4 MX.

The type of sign and its position is randomized. Jetson AGX Xavier series modules enable new levels of compute density, power efficiency, and AI inferencing capabilities at the edge. Retrieved Jetson is a low-power system and is designed for accelerating machine learning applications. JetPack DeepStream Isaac. These units aim to different markets and tasks. Developer Kit Module. Single-board computer and single-board microcontroller.

Jetson Support and Ecosystem. Jetson Modules. To specify image processing pipeline for testing, we consider a basic camera application as a good example for benchmarking. GeForce 20 Can you suggest any other tools which work on arm? GoForce Drive Jetson Tegra. Jetson TX1. The memory bandwidth is faster on the Cortex-A57, Its high-performance, low-power computing for deep learning and computer vision makes it the ideal platform for mobile compute-intensive projects. I consent to having Fastvideo LLC collect my name and email.

Nvidia Tesla DGX. GeForce 20 GeForce 8 9 There was also a lot of change in caching on the bit version which in itself made bit a lot smarter with cache. GeForce 3 4 Ti FX 6 7. Apache Hadoop Linaro. Some of them are system related Here are a few of the reasons, but certainly not all. GeForce M 10 There are a wide variety of factors for the better performance of the Cortex-A57 over the Cortex-A Jetson Nano is a small, powerful computer for embedded AI systems and IoT that delivers the power of modern AI in a low-power platform.

The algorithm is based on artificial neural networks that use deep learning methods for efficiency. Namespaces Article Talk. Here you can get the document with the benchmarks. I consent to having Fastvideo LLC collect my name and email. I am aware that the A57 is a 64 bit processor, the L4T will use 32 bit though… In performance tests with nbench the K1 processor gets a better result. I and other did some tests using Cuda 7. You can also perform various tests on images with different resolutions to see how the performance depends on image size, content and other parameters. Jetson is a low-power system and is designed for accelerating machine learning applications.

JetPack 3. Nvidia Jetson is a series of embedded computing boards from Nvidia. Since object detection and recognition does introduce some computational complexity, performing it in real-time on embedded platforms used to be difficult — that is, before platforms like Nvidia Tegra K1 and X1 emerged, bringing down the performance-per-watt for such applications to levels acceptable for packing it into a relatively small physical footprint. Resources Ecosystem Projects. There was also a lot of change in caching on the bit version which in itself made bit a lot smarter with cache. Hopefully 64bit L4T will improve TX1 performance significantly. You can also perform various tests on images with different resolutions to see how the performance depends on image size, content and other parameters. GeForce M 10

Nvidia Quadro Quadro Plex. Jetson Ecosystem Partners Get to market faster with software, hardware, and sensor products and services available from Jetson ecosystem and distribution partners. From Wikipedia, the free encyclopedia. The official Nvidia download page bears an entry for JetPack 3. GeForce 8 9 Click to Expand. Nvidia Jetson is a series of embedded computing boards from Nvidia. Cherrypal Simputer. For these NVIDIA Jetson modules we've done performance benchmarking for the following standard image processing tasks which are specific for camera applications: white balance, demosaic debayer , color correction, resize, jpeg encoding, etc.

Jetson Modules. Skip to main content. Since object detection and recognition does introduce some computational complexity, performing it in real-time on embedded platforms used to be difficult — that is, before platforms like Nvidia Tegra K1 and X1 emerged, bringing down the performance-per-watt for such applications to levels acceptable for packing it into a relatively small physical footprint. Series of embedded computing boards from Nvidia. Jetson Xavier NX. NV1 NV2. Apache Hadoop Linaro. This form collects your name and email.

Jetson AGX Xavier series modules enable new levels of compute density, power efficiency, and AI inferencing capabilities at the edge. Nvidia Tesla DGX. GeForce 2 4 MX. Hopefully 64bit L4T will improve TX1 performance significantly. But there is a new kid on the block now: the TX1, and it has been obvious that as with every new generation, the new Tegra would provide a significant performance improvement over its predecessor. Its high-performance, low-power computing for deep learning and computer vision makes it the ideal platform for mobile compute-intensive projects. Architecture of the setup The application is based on the OpenCV library, which supports camera configuration and frame capture, does the road sign recognition and prepares images for h encoding and streaming to the paired platform we are using Microsoft Surface for demos. Jetson Xavier NX. With Jetson, customers can accelerate all modern AI networks, easily roll out new features, and leverage the same software for different products and applications. From Wikipedia, the free encyclopedia.

I suspect the quad core A57 at 1. First, the Cortex-A57 is 64 bit versus the 32 bit Cortex-A We've released the software for GPU-based camera application on Github and it's available to download both binaries and source codes for gpu camera sample project. Benchmarks Roadmap Buy. Jetson TX1 The world's first supercomputer on a module, Jetson TX1 delivers the performance and power efficiency needed for visual computing applications. Is someone from Nvidia able to tell if the 1TFlop you tell the TX1 has was measured with 32 or 64 bit userland? Get to market faster with software, hardware, and sensor products and services available from Jetson ecosystem and distribution partners. As soon as H. The world's first supercomputer on a module, Jetson TX1 delivers the performance and power efficiency needed for visual computing applications. Apart from full image processing pipeline on GPU for still images from SSD and for live camera output, there are options for streaming and for glass-to-glass G2G measurements to evaluate real latency for camera system on Jetson.

Architecture of the setup The application is based on the OpenCV library, which supports camera configuration and frame capture, does the road sign recognition and prepares images for h encoding and streaming to the paired platform we are using Microsoft Surface for demos. Jetson Nano. GoForce Drive Jetson Tegra. Apart from full image processing pipeline on GPU for still images from SSD and for live camera output, there are options for streaming and for glass-to-glass G2G measurements to evaluate real latency for camera system on Jetson. Jetson Support Resources Detailed hardware design collateral, software samples and documentation, and an active Jetson developer community are here to help. There was also a lot of change in caching on the bit version which in itself made bit a lot smarter with cache. GeForce 8 9 Possibly a bit userspace would run faster.

Apache Hadoop Linaro. The Cortex-A57 can fetch, decode, and dispatch three instructions per clock cycle, and it executes instructions out of program order to improve throughput. GoForce Drive Jetson Tegra. Comparison of single-board computers. These units aim to different markets and tasks. Here we've compared just the basic set of image processing modules from Fastvideo SDK to let Jetson developers evaluate expected performance before building their imaging applications. Possibly a bit userspace would run faster. Develop Software Tools Production. Check out our Privacy Policy on how we protect and manage your personal data. Total processing time is calculated for values from gray rows of the table.

For these NVIDIA Jetson modules we've done performance benchmarking for the following standard image processing tasks which are specific for camera applications: white balance, demosaic debayer , color correction, resize, jpeg encoding, etc. Is someone from Nvidia able to tell if the 1TFlop you tell the TX1 has was measured with 32 or 64 bit userland? I suspect the quad core A57 at 1. Apache Hadoop Linaro. Wikimedia Commons. It's not a full set of Fastvideo SDK features, but this is just an example to see what's the performance that we could get from each Jetson. Jetson Module Lineup. There was also a lot of change in caching on the bit version which in itself made bit a lot smarter with cache.

GeForce M 10 Linux-powered devices. Here we publish performance benchmarks for available Jetson modules. The QNX operating system also available for the Jetson platform, though it is not widely announced. Hidden categories: Articles with short description Short description matches Wikidata. Help Learn to edit Community portal Recent changes Upload file. This form collects your name and email. Since object detection and recognition does introduce some computational complexity, performing it in real-time on embedded platforms used to be difficult — that is, before platforms like Nvidia Tegra K1 and X1 emerged, bringing down the performance-per-watt for such applications to levels acceptable for packing it into a relatively small physical footprint.

Namely the package qnx-V3Q These units aim to different markets and tasks. But there is a new kid on the block now: the TX1, and it has been obvious that as with every new generation, the new Tegra would provide a significant performance improvement over its predecessor. There are a wide variety of factors for the better performance of the Cortex-A57 over the Cortex-A There are success reports of installing and running specific QNX packages on certain Nvidia Jetson board variants. Apache Hadoop Linaro. The Cortex-A57 can fetch, decode, and dispatch three instructions per clock cycle, and it executes instructions out of program order to improve throughput. EDIT: Changed to fix an obvious wetware failure on my part wetware being my brain.

Cherrypal Simputer. Retrieved We haven't tested Jetson H. Possibly a bit userspace would run faster. Single-board computer and single-board microcontroller. Wikimedia Commons. Apart from full image processing pipeline on GPU for still images from SSD and for live camera output, there are options for streaming and for glass-to-glass G2G measurements to evaluate real latency for camera system on Jetson. Hopefully 64bit L4T will improve TX1 performance significantly.

Retrieved Jetson AGX Xavier series modules enable new levels of compute density, power efficiency, and AI inferencing capabilities at the edge. Actions Allwinner Ax Exynos i. Project Denver. These units aim to different markets and tasks. On the other hand, the Cortex-A15 executes 2 instructions per clock cycle with instructions executing in program order. GeForce 3 4 Ti FX 6 7. Hopefully 64bit L4T will improve TX1 performance significantly. Technical Specifications.

Jetson Ecosystem Partners Get to market faster with software, hardware, and sensor products and services available from Jetson ecosystem and distribution partners. Click to Expand. We haven't tested Jetson H. Here we've compared just the basic set of image processing modules from Fastvideo SDK to let Jetson developers evaluate expected performance before building their imaging applications. To specify image processing pipeline for testing, we consider a basic camera application as a good example for benchmarking. Jetson Support Resources Detailed hardware design collateral, software samples and documentation, and an active Jetson developer community are here to help. Cherrypal Simputer. Series of embedded computing boards from Nvidia. Skip to main content. Project Denver.

Nvidia Tesla DGX. Wikimedia Commons. With Jetson, customers can accelerate all modern AI networks, easily roll out new features, and leverage the same software for different products and applications. There was also a lot of change in caching on the bit version which in itself made bit a lot smarter with cache. As you may remember from previous posts , our involvement in the automotive research platform codenamed MOPED , originally created by the Software and Systems Engineering Laboratory SSE of SICS, has been primarily revolving around introducing the Nvidia Tegra as its main processing unit, to better reflect the typical setup present in many modern cars. Download as PDF Printable version. Resources Ecosystem Projects. Jetson Ecosystem Partners Get to market faster with software, hardware, and sensor products and services available from Jetson ecosystem and distribution partners. Namespaces Article Talk. New designs should use Jetson TX2 4GB to run neural networks with double the compute performance or double the power efficiency of Jetson TX1—at the same price.

There are success reports of installing and running specific QNX packages on certain Nvidia Jetson board variants. There was also a lot of change in caching on the bit version which in itself made bit a lot smarter with cache. Retrieved Can you suggest any other tools which work on arm? Jetson Nano. I and other did some tests using Cuda 7. Jetson Support Resources Detailed hardware design collateral, software samples and documentation, and an active Jetson developer community are here to help. On the other hand, the Cortex-A15 executes 2 instructions per clock cycle with instructions executing in program order.

Here you can get the document with the benchmarks. Nvidia Quadro Quadro Plex. GeForce 20 Jetson Support and Ecosystem. The official Nvidia download page bears an entry for JetPack 3. Table 1. Atom Jaguar -based Puma -based Quark. This is done to show maximum performance benchmarks for specified set of image processing modules which correspond to real life camera applications. Get to market faster with software, hardware, and sensor products and services available from Jetson ecosystem and distribution partners. Help Learn to edit Community portal Recent changes Upload file.

The algorithm is based on artificial neural networks that use deep learning methods for efficiency. Intelligent machine OEMs and AI application developers create breakthrough products with Jetson in the fields of manufacturing, logistics, retail, service, agriculture, smart city, and healthcare and life sciences. For these NVIDIA Jetson modules we've done performance benchmarking for the following standard image processing tasks which are specific for camera applications: white balance, demosaic debayer , color correction, resize, jpeg encoding, etc. Namely the package qnx-V3Q In performance tests with nbench the K1 processor gets a better result. Detailed hardware design collateral, software samples and documentation, and an active Jetson developer community are here to help. Retrieved For the comparison, we focused on road sign recognition, as it makes for a nice and contained use case. GeForce M 10

This is done to show maximum performance benchmarks for specified set of image processing modules which correspond to real life camera applications. Nvidia Jetson is a series of embedded computing boards from Nvidia. Jetson Module Lineup. The setup includes signs of different shapes and sizes which are displayed on the external monitor. Jetson TX1. The type of sign and its position is randomized. The Cortex-A57 can fetch, decode, and dispatch three instructions per clock cycle, and it executes instructions out of program order to improve throughput. These units aim to different markets and tasks.

Retrieved The classification thread has to solve two problems: finding signs in the surrounding anvironment and recognizing their type. Skip to main content. Resources Ecosystem Projects. It's not a full set of Fastvideo SDK features, but this is just an example to see what's the performance that we could get from each Jetson. JetPack DeepStream Isaac. New designs should use Jetson TX2 4GB to run neural networks with double the compute performance or double the power efficiency of Jetson TX1—at the same price. Total processing time is calculated for values from gray rows of the table. For these NVIDIA Jetson modules we've done performance benchmarking for the following standard image processing tasks which are specific for camera applications: white balance, demosaic debayer , color correction, resize, jpeg encoding, etc.

It can run multiple modern neural networks in parallel and process data from multiple high-resolution sensors—a requirement for full AI systems. Namely the package qnx-V3Q Linux-powered devices. Can you suggest any other tools which work on arm? There are success reports of installing and running specific QNX packages on certain Nvidia Jetson board variants. To specify image processing pipeline for testing, we consider a basic camera application as a good example for benchmarking. Click to Expand. I consent to having Fastvideo LLC collect my name and email. Hopefully 64bit L4T will improve TX1 performance significantly. Here we publish performance benchmarks for available Jetson modules.

As soon as H. But there is a new kid on the block now: the TX1, and it has been obvious that as with every new generation, the new Tegra would provide a significant performance improvement over its predecessor. Linux-powered devices. Project Denver. These units aim to different markets and tasks. Help Learn to edit Community portal Recent changes Upload file. I consent to having Fastvideo LLC collect my name and email. Jetson Modules. Get to market faster with software, hardware, and sensor products and services available from Jetson ecosystem and distribution partners. Developer Kit Module.

In performance tests with nbench the K1 processor gets a better result. That is with L4T on both systems, which means the A57 is running in bit mode in userspace. For the comparison, we focused on road sign recognition, as it makes for a nice and contained use case. Help Learn to edit Community portal Recent changes Upload file. This form collects your name and email. With Jetson, customers can accelerate all modern AI networks, easily roll out new features, and leverage the same software for different products and applications. It includes the Linux for Tegra L4T operating system and other tools. Click to Expand. Here we publish performance benchmarks for available Jetson modules. Jetson Ecosystem Partners Get to market faster with software, hardware, and sensor products and services available from Jetson ecosystem and distribution partners.

Jetson Nano. The Cortex-A57 can fetch, decode, and dispatch three instructions per clock cycle, and it executes instructions out of program order to improve throughput. The classification thread has to solve two problems: finding signs in the surrounding anvironment and recognizing their type. The QNX operating system also available for the Jetson platform, though it is not widely announced. Help Learn to edit Community portal Recent changes Upload file. Jetson Nano is a small, powerful computer for embedded AI systems and IoT that delivers the power of modern AI in a low-power platform. JetPack 3. Single-board computer and single-board microcontroller. GeForce 2 4 MX. The algorithm is based on artificial neural networks that use deep learning methods for efficiency.

Resources Ecosystem Projects. Jetson TX1 The world's first supercomputer on a module, Jetson TX1 delivers the performance and power efficiency needed for visual computing applications. From Wikipedia, the free encyclopedia. You can also perform various tests on images with different resolutions to see how the performance depends on image size, content and other parameters. The Cortex-A57 can fetch, decode, and dispatch three instructions per clock cycle, and it executes instructions out of program order to improve throughput. The classification thread has to solve two problems: finding signs in the surrounding anvironment and recognizing their type. The memory bandwidth is faster on the Cortex-A57, Jetson Support and Ecosystem.

There are a wide variety of factors for the better performance of the Cortex-A57 over the Cortex-A It includes the Linux for Tegra L4T operating system and other tools. For these NVIDIA Jetson modules we've done performance benchmarking for the following standard image processing tasks which are specific for camera applications: white balance, demosaic debayer , color correction, resize, jpeg encoding, etc. Jetson Support and Ecosystem. Atom Jaguar -based Puma -based Quark. Check out our Privacy Policy on how we protect and manage your personal data. Jetson Nano. Actions Allwinner Ax Exynos i. This is done to show maximum performance benchmarks for specified set of image processing modules which correspond to real life camera applications.

With Jetson, customers can accelerate all modern AI networks, easily roll out new features, and leverage the same software for different products and applications. Cherrypal Simputer. Here we publish performance benchmarks for available Jetson modules. Click to Expand. Series of embedded computing boards from Nvidia. Linux-powered devices. Here we've compared just the basic set of image processing modules from Fastvideo SDK to let Jetson developers evaluate expected performance before building their imaging applications. I and other did some tests using Cuda 7. We've released the software for GPU-based camera application on Github and it's available to download both binaries and source codes for gpu camera sample project. GeForce M 10

Jetson Ecosystem Partners Get to market faster with software, hardware, and sensor products and services available from Jetson ecosystem and distribution partners. Cherrypal Simputer. The type of sign and its position is randomized. EDIT: Changed to fix an obvious wetware failure on my part wetware being my brain. Check out our Privacy Policy on how we protect and manage your personal data. Linux-powered devices. Actions Allwinner Ax Exynos i. We've released the software for GPU-based camera application on Github and it's available to download both binaries and source codes for gpu camera sample project. Table 1. I and other did some tests using Cuda 7.

This is done to show maximum performance benchmarks for specified set of image processing modules which correspond to real life camera applications. Cherrypal Simputer. As you may remember from previous posts , our involvement in the automotive research platform codenamed MOPED , originally created by the Software and Systems Engineering Laboratory SSE of SICS, has been primarily revolving around introducing the Nvidia Tegra as its main processing unit, to better reflect the typical setup present in many modern cars. Jetson Ecosystem Partners Get to market faster with software, hardware, and sensor products and services available from Jetson ecosystem and distribution partners. Develop Software Tools Production. Namely the package qnx-V3Q As soon as H. I am aware that the A57 is a 64 bit processor, the L4T will use 32 bit though… In performance tests with nbench the K1 processor gets a better result. Can you suggest any other tools which work on arm? Hopefully 64bit L4T will improve TX1 performance significantly.

Linux-powered devices. Here you can get the document with the benchmarks. Jetson Nano is a small, powerful computer for embedded AI systems and IoT that delivers the power of modern AI in a low-power platform. From Wikipedia, the free encyclopedia. The type of sign and its position is randomized. Hidden categories: Articles with short description Short description matches Wikidata. As you may remember from previous posts , our involvement in the automotive research platform codenamed MOPED , originally created by the Software and Systems Engineering Laboratory SSE of SICS, has been primarily revolving around introducing the Nvidia Tegra as its main processing unit, to better reflect the typical setup present in many modern cars. As soon as H.

Jetson AGX Xavier series modules enable new levels of compute density, power efficiency, and AI inferencing capabilities at the edge. GeForce 8 9 GeForce 3 4 Ti FX 6 7. There are a wide variety of factors for the better performance of the Cortex-A57 over the Cortex-A We've released the software for GPU-based camera application on Github and it's available to download both binaries and source codes for gpu camera sample project. GeForce M 10 Single-board computer and single-board microcontroller. The world's first supercomputer on a module, Jetson TX1 delivers the performance and power efficiency needed for visual computing applications.

Namely the package qnx-V3Q Detailed hardware design collateral, software samples and documentation, and an active Jetson developer community are here to help. Possibly a bit userspace would run faster. Technical Specifications. Retrieved It can run multiple modern neural networks in parallel and process data from multiple high-resolution sensors—a requirement for full AI systems. New designs should use Jetson TX2 4GB to run neural networks with double the compute performance or double the power efficiency of Jetson TX1—at the same price. Jetson Support Resources Detailed hardware design collateral, software samples and documentation, and an active Jetson developer community are here to help. I suspect the quad core A57 at 1.

You can also perform various tests on images with different resolutions to see how the performance depends on image size, content and other parameters. Here we've compared just the basic set of image processing modules from Fastvideo SDK to let Jetson developers evaluate expected performance before building their imaging applications. Technical Specifications. Benchmarks Roadmap Buy. Linux-powered devices. The memory bandwidth is faster on the Cortex-A57, Skip to main content. To specify image processing pipeline for testing, we consider a basic camera application as a good example for benchmarking. For these NVIDIA Jetson modules we've done performance benchmarking for the following standard image processing tasks which are specific for camera applications: white balance, demosaic debayer , color correction, resize, jpeg encoding, etc.

For these NVIDIA Jetson modules we've done performance benchmarking for the following standard image processing tasks which are specific for camera applications: white balance, demosaic debayer , color correction, resize, jpeg encoding, etc. Table 1. Its high-performance, low-power computing for deep learning and computer vision makes it the ideal platform for mobile compute-intensive projects. Get to market faster with software, hardware, and sensor products and services available from Jetson ecosystem and distribution partners. I suspect the quad core A57 at 1. With Jetson, customers can accelerate all modern AI networks, easily roll out new features, and leverage the same software for different products and applications. This is done to show maximum performance benchmarks for specified set of image processing modules which correspond to real life camera applications. GeForce 20 Jetson Module Lineup.

JetPack 3. JetPack DeepStream Isaac. The world's first supercomputer on a module, Jetson TX1 delivers the performance and power efficiency needed for visual computing applications. Jetson Support Resources Detailed hardware design collateral, software samples and documentation, and an active Jetson developer community are here to help. Comparison of single-board computers. Linux-powered devices. The setup includes signs of different shapes and sizes which are displayed on the external monitor. Is someone from Nvidia able to tell if the 1TFlop you tell the TX1 has was measured with 32 or 64 bit userland? Download as PDF Printable version. For these NVIDIA Jetson modules we've done performance benchmarking for the following standard image processing tasks which are specific for camera applications: white balance, demosaic debayer , color correction, resize, jpeg encoding, etc.