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Nvidia Drive

Nvidia Drive is a science topic covered in the lgStudy science library. This page brings together a partial reference excerpt, illustrations, worked examples, real-world applications and a short study plan, so you can understand Nvidia Drive rather than just read about it. In short: Nvidia Drive is a computer platform by Nvidia, aimed at providing autonomous car and driver assistance functionality powered by deep learning. The platform was introduced at the Consumer Electronics Show (CES) in Las Vegas in January 2015.

Nvidia Drive — main illustration
Nvidia Drive — illustration

Key takeaways

  • Nvidia Drive belongs to science; place it in that map before memorising details.
  • Learn the definition first, then one example that makes the definition concrete.
  • Connect Nvidia Drive to a quantity you can measure, compute or draw — that is where exam questions come from.
  • Reproduce the core statement of Nvidia Drive from memory before moving on to harder problems.

Reference excerpt

Nvidia Drive is a computer platform by Nvidia, aimed at providing autonomous car and driver assistance functionality powered by deep learning. The platform was introduced at the Consumer Electronics Show (CES) in Las Vegas in January 2015. An enhanced version, the Drive PX 2 was introduced at CES a year later, in January 2016. The closely platform related software release program at some point in time was branded NVIDIA DRIVE Hyperion along with a revision number helping to match with the generation of hardware it is created for - and also creating ready to order bundles under those term. In former times there were only the terms Nvidia Drive SDK for the developer package and sub-included Nvidia Drive OS for the system software (aka OS) that came with the evaluation platforms or could be downloaded for OS switching and updating later on.

Hardware and semiconductors

Maxwell based The first of Nvidia's autonomous chips was announced at CES 2015, based on the Maxwell GPU microarchitecture. The line-up consisted of two platforms:

Drive CX The Drive CX was based on a single Tegra X1 SoC (System on a Chip) and was marketed as a digital cockpit computer, providing a rich dashboard, navigation and multimedia experience. Early Nvidia press releases reported that the Drive CX board will be capable of carrying either a Tegra K1 or a Tegra X1.

Drive PX

The first version of Drive PX is based on two Tegra X1 SoCs, and was an initial development platform targeted at (semi-)autonomous driving cars.

Pascal based Drive PX platforms based on the Pascal GPU microarchitecture were first announced at CES 2016. This time only a new version of Drive PX was announced, but in multiple configurations.

Drive PX 2 The Nvidia Drive PX 2 is based on one or two Tegra X2 SoCs where each SoC contains 2 Denver cores, 4 ARM A57 cores and a GPU from the Pascal generation. There are two real world board configurations:

for AutoCruise: 1× Tegra X2 + 1 Pascal GPU for AutoChauffeur: 2× Tegra X2 + 2 Pascal GPU's There is further the proposal from Nvidia for fully autonomous driving by means of combining multiple items of the AutoChauffeur board variant and connecting these boards using e.g. UART, CAN, LIN, FlexRay, USB, 1 Gbit Ethernet or 10 Gbit Ethernet. For any derived custom PCB design the option of linking the Tegra X2 Processors via some PCIe bus bridge is further available, according to board block diagrams that can be found on the web. All Tesla Motors vehicles manufactured from mid-October 2016 include a Drive PX 2, which will be used for neural net processing to enable Enhanced Autopilot and full self-driving functionality. Other applications are Roborace. Disassembling the Nvidia-based control unit from a recent Tesla car showed that a Tesla was using a modified single-chip Drive PX 2 AutoCruise, with a GP106 GPU added as a MXM Module. The chip markings gave strong hints for the Tegra X2 Parker as the CPU SoC.

Volta and Turing based Systems based on the Volta GPU microarchitecture and Turing GPU microarchitecture were first announced at CES 2017. It was originally named Drive PX, but later changed to DRIVE AGX.

DRIVE AGX Xavier The first Volta based Drive PX system was announced at CES 2017 as the Xavier AI Car Supercomputer. It was re-presented at CES 2018 as Drive PX Xavier. Initial reports of the Xavier SoC suggested a single chip with similar processing power to the Drive PX 2 Autochauffeur system. However, in 2017 the performance of the Xavier-based system was later revised upward, to 50% greater than Drive PX 2 Autochauffeur system. Drive PX Xavier is supposed to deliver 30 INT8 TOPS of performance while consuming only 30 watts of power. This spreads across two distinct units, the iGPU with 20 INT8 TOPS as published early and the somewhat later on announced, newly introduced DLA that provided an additional 10 INT8 TOPS.

DRIVE AGX Pegasus In October 2017 Nvidia and partner development companies announced the Drive PX Pegasus system, based upon two Xavier CPU/iGPU devices and two Turing generation dGPUs. The companies stated the third generation Drive PX system would be capable of Level 5 autonomous driving, with a total of 320 INT8 TOPS of AI computational power and a 500 Watts TDP.

Ampere based

DRIVE AGX Orin The Drive AGX Orin board family was announced on December 18, 2019, at GTC China 2019. On May 14, 2020, Nvidia announced that Orin would be utilizing the new Ampere GPU microarchitecture and would begin sampling for manufacturers in 2021 and be available for production in 2022. Follow up variants are expected to be further equipped with chip models and/or modules from the Tegra Orin SoC.

Ada Lovelace based

DRIVE Atlan (cancelled) Nvidia announced the SoC codenamed Atlan on April 12, 2021 at GTC 2021. Nvidia announced the cancellation of Atlan on September 20, 2022, which was supposed to be equipped with a Grace-Next CPU, and an Ada Lovelace based GPU, and Nvidia announced that their next SoC was called Thor.

Blackwell based

DRIVE AGX Thor Announced on September 20, 2022, Nvidia Drive AGX Thor comes equipped with an Arm Neoverse V3AE CPU, and a Blackwell based GPU, which was announced on March 18, 2024. It features 8-bit floating point support (FP8) and delivers 1000 Sparse INT8 TOPS, 1000 Sparse FP8 TFLOPS or 500 Sparse FP16 TFLOPS of performance. Two Thor SoCs can be connected via NVLink-C2C. BYD, Hyper, XPENG, Li Auto and ZEEKR have said to be use DRIVE AGX Thor in their vehicles. The Lynk & Co 900 is the first production vehicle to feature the DRIVE AGX Thor SoC.

… excerpt ends here. Continue reading the full article.

Worked examples

Example 1 — a first encounter with Nvidia Drive

Start with the simplest possible case. Write down what Nvidia Drive claims or describes in one sentence, then invent the smallest concrete situation in which that sentence is true. In science, the smallest case is usually a single object, a single equation or a single measurement. Check that every symbol or term in your sentence has a meaning in that case.

Example 2 — changing one variable

Take the situation from Example 1 and change exactly one quantity: double it, halve it, or set it to zero. Predict what should happen to Nvidia Drive before you calculate. Comparing your prediction with the result is the fastest way to find out whether you understand the idea or only the words.

Example 3 — an exam-style question

Typical questions about Nvidia Drive ask you to (a) state it precisely, (b) apply it to given data, and (c) explain a limitation. Practise writing all three answers in under five minutes; the third part is what separates a full-mark answer from an average one.

Applications of Nvidia Drive

In research
Nvidia Drive appears in science research whenever the underlying quantities have to be modelled precisely. Papers usually cite it as a starting assumption and then explore where it breaks down.
In technology and industry
Engineering practice reuses Nvidia Drive in design rules, simulations and safety margins. Knowing the idea lets you read a specification sheet and understand why the numbers look the way they do.
In the classroom
Nvidia Drive is common in secondary-school and first-year university syllabi. It links to neighbouring topics Automotive electronics, Neural processing units, Nvidia products, so understanding it makes those chapters shorter.
In everyday life
Look for Nvidia Drive outside the textbook — in sport, cooking, traffic, electronics or the sky above you. An example you found yourself is remembered far longer than one you were given.

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How to study Nvidia Drive in 20 minutes

  1. Read the reference excerpt below once, without taking notes.
  2. Close the page and write down what Nvidia Drive means in your own words.
  3. Compare your version with the excerpt and mark what you missed.
  4. Work through the three examples above with pen and paper.
  5. Explain Nvidia Drive out loud to somebody else — or to Teacher Smith in the lgStudy chat.

Frequently asked questions

What is Nvidia Drive in simple terms?

Nvidia Drive is a computer platform by Nvidia, aimed at providing autonomous car and driver assistance functionality powered by deep learning. The platform was introduced at the Consumer Electronics Show (CES) in Las Vegas in January 2015.

Why does Nvidia Drive matter?

Because it connects several science ideas at once: it gives you a definition you can apply, a quantity you can calculate, and a way to check whether a result is plausible.

How should I study Nvidia Drive?

Read the excerpt, restate it from memory, then work through the examples and applications listed on this page. The five-step study plan above takes about twenty minutes.

What does this page cover?

It gives you a compact reference excerpt plus original lgStudy explanations, examples, applications and study material on Nvidia Drive.

Tags

  • Automotive electronics
  • Neural processing units
  • Nvidia products

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