Tag Archives: NVIDIA Jetpack 3.0

The NVIDIA Jetson TX2 (Pascal) Tech Report

The NVIDIA Jetson TX2 (Pascal) Tech Report

NVIDIA just announced the Jetson TX2 embedded AI supercomputer, based on the latest NVIDIA Pascal microarchitecture. It promises to offer twice the performance of the previous-generation Jetson TX1, in the same package. In this tech report, we will share with you the full details of the new Pascal-based NVIDIA Jetson TX2!

 

GPUs In Artificial Intelligence

Artificial intelligence is the new frontier in GPU compute technology. Whether they are used to power training or inference engines, AI research has benefited greatly from the massive amounts of compute power in modern GPUs.

The market is led by NVIDIA with their Tesla accelerators that run on their proprietary CUDA platform. AMD, on the other hand, is a relative newcomer with their Radeon Instinct accelerators designed to run on the open-source ROCm (Radeon Open Compute) platform.

 

The NVIDIA Jetson

GPUs today offer so much compute performance that NVIDIA has been able to create the NVIDIA Jetson family of embedded AI supercomputers. They differ from their Tesla big brother in their size, power efficiency and purpose. The NVIDIA Jetson modules are specifically built for “inference at the edge” or “AI at the edge“.

 

Unlike AI processing in the datacenters or in the cloud, AI in the edge refers to autonomous artificial intelligence processing, where there is poor or no Internet access or access must be restricted for privacy or security reasons. Therefore, the processor must be powerful enough for the AI application to run autonomously.

Whether it’s to automate robots in a factory, or to tackle industrial accidents like at the Fukushima Daiichi nuclear plant, AI at the edge is meant to allow for at least some autonomous capability right in the field. The AI in the edge processors must also be frugal in using power, as power or battery life is often limited.

[adrotate banner=”5″]

Hence, processors designed for AI on the edge applications must be small, power-efficient and yet, fast enough to run AI inference in real time. The NVIDIA Jetson family of embedded AI supercomputers promises to tick all of those boxes. Let’s take a look :

Next Page > The NVIDIA Jetson TX2, Specification Comparison, Price & Availability

 

Support Tech ARP!

If you like our work, you can help support our work by visiting our sponsors, participating in the Tech ARP Forums, or even donating to our fund. Any help you can render is greatly appreciated!

The NVIDIA Jetson TX2

The NVIDIA Jetson TX2 is the second-generation Jetson embedded AI module, based on the latest NVIDIA Pascal microarchitecture. It supersedes (but not replace) the previous-generation Jetson TX1, which was built on the NVIDIA Maxwell microarchitecture and released in November 2015.

Thanks to the faster and more power-efficient Pascal microarchitecture, the NVIDIA Jetson TX2 promises to be twice as energy-efficient as the Jetson TX1.

This means the developers switching to the Jetson TX2 can now opt to maximise power efficiency, or to maximise performance. In the Max-Q mode, the Jetson TX2 will use less than 7.5 W, and offer Jetson TX1-equivalent performance. In the Max-P mode, the Jetson TX2 will use less than 15 W, and offer up to twice the performance of the Jetson TX1.

 

NVIDIA Jetson Specification Comparison

The NVIDIA Jetson modules are actually built around the NVIDIA Tegra SoCs, instead of their GeForce GPUs. The Tegra SoC is a System On A Chip, which integrates an ARM CPU, an NVIDIA GPU, a chipset and a memory controller on a single package.

The Tegra SoC and the other components on a 50 x 87 mm board are what constitutes the NVIDIA Jetson module. The Jetson TX1 uses the Tegra X1 SoC, while the new Jetson TX2 uses the Tegra P1 SoC.

For those who have been following our coverage of the AMD Radeon Instinct, and its support for packed math, the NVIDIA Jetson TX2 and TX1 modules support FP16 operations too.

[adrotate banner=”5″]

 

NVIDIA Jetson TX2 Price & Availability

The NVIDIA Jetson TX2 Developer Kit is available for pre-order in the US and Europe right now, with a US$ 599 retail price and a US$ 299 education price. Shipping will start on March 14, 2017. This developer’s kit will be made available in APAC and other regions in April 2017.

The NVIDIA Jetson TX2 module itself will only be made available in the second quarter of 2017. It will be priced at US$ 399 per module, in quantities of 1,000 modules or more.

Note that the Jetson TX2 modules are exactly the same size and uses the same 400-pin connector. They are drop-in compatible replacements for the Jetson TX1 modules.

Next Page > NVIDIA Jetson TX1 Price Adjustments, NVIDIA Jetpack 3.0, The Slides

 

Support Tech ARP!

If you like our work, you can help support our work by visiting our sponsors, participating in the Tech ARP Forums, or even donating to our fund. Any help you can render is greatly appreciated!

NVIDIA Jetson TX1 Price Adjustments

With the launch of the Jetson TX2, NVIDIA is adjusting the price of the Jetson TX1. The Jetson TX1 will continue to sell alongside the new Jetson TX2.

The NVIDIA Jetson TX1 Developer Kit has been reduced to US$ 499, down from US$ 599.

The NVIDIA Jetson TX1 production has been reduced to US$ 299, down from US$ 399. Again, this is in quantities of 1,000 modules or more.

 

NVIDIA Jetpack 3.0

The NVIDIA Jetson is more than just a processor module. It is a platform that is made up of developer tools and codes, as well as APIs. Like AMD offers their MIOpen deep learning library, NVIDIA offers Jetpack.

In conjunction with the launch of the Jetson TX2, NVIDIA also announced the NVIDIA Jetpack 3.0. It promises to offer twice the system performance of Jetpack 2.3.

Jetpack 3.0 is not just for the new Jetson TX2. It will offer a nice boost in performance for existing Jetson TX1 users and applications.

[adrotate banner=”5″]

 

The Presentation Slides

For those who want the full set of NVIDIA Jetson TX2 slides, here they are :

 

Support Tech ARP!

If you like our work, you can help support our work by visiting our sponsors, participating in the Tech ARP Forums, or even donating to our fund. Any help you can render is greatly appreciated!