How Does A Changing Automotive Ecosystem Affect Tier-1 Suppliers?

More focus on pre-silicon verification for automotive chips.


Tier-1 automotive suppliers have an enormous opportunity in the development of autonomous vehicles (AVs). sees these vehicles contributing $7 trillion in economic activity by the year 2050. But this opportunity comes with a challenge: the whole supply chain is being disrupted by new participants and new technologies that are making these AVs possible. Semiconductor companies and specialist OEMs are major new players, and newcomers include both large, well-established companies and small start-ups.

These changes affect the role of tier-1 suppliers, and those players must adapt to ensure that they continue to add solid value to the supply chain. One important step in that direction is the adoption of new system verification strategies as a response to the increased difficulties posed by vehicles that have to be safe enough to drive themselves. Siemens’ PAVE360 program, with its focus on emulation, provides a straightforward way for tier-1 suppliers to reinforce the value they provide.

A supply chain in flux
The electronic content and computing power required for AVs is moving the architecture from one containing many small, independent ECUs to a more centralized approach with higher-powered multicore processors running extensive suites of software. This brings new players into the picture, and the tight collaboration required means that the supplier ecosystem must be reinvented to reflect these new contributions.

Tier-1 suppliers, in particular, will encounter a number of specific new challenges:

  • Vehicles are being developed by large technology companies like Google, Amazon, and Baidu. These are complete newcomers to the supply chain, and they may weaken the traditional supply chain by doing things differently.
  • Automotive OEMs are increasingly working directly with tier-2 semiconductor companies like NXP and Nvidia – a move that could threaten to squeeze out the tier-1 supplier.
  • Electric-vehicle start-ups, especially in China, are opening up new markets. They may try to bypass tier-1 suppliers in the process.
  • Some specialist OEMs like Tesla are integrating vertically, leaving out the entire traditional supply chain.

This changing environment is forcing all players to rethink their roles in the supply chain. Sticking to the old models will decidedly not be a winning strategy. In order to understand where changes and new opportunities lie, we need to understand the implications of this new electronic push within vehicles.

Silicon content is booming
The increased electronics is directly reflected in the amount of silicon in an autonomous vehicle, and this poses a unique challenge to tier-1 systems designers. There is specific growth in the use of systems-on-chip, or SoCs – sophisticated, highly integrated chips that include custom computing capability. No SoC can be complete without considering the software that the SoC will execute. That software adds a new dimension to silicon verification.

Traditional automotive flows, which involved smaller islands of electronics (ECUs), made use of four different tools for verification. New flows are adding a fifth.

  • Virtual prototypes leverage high-level models of hardware behavior. While these models execute quickly, their accuracy is insufficient for full verification.
  • Hardware-in-the-loop (HIL) is an option once hardware is available and stable. Because this relies on existing hardware rather than a fully instrumented verification testbench, stimulus tends to be non-deterministic, and, when things go wrong, debugging is difficult. While useful for checking out newly-built integrated circuits, it’s insufficient for thorough verification.
  • Software simulation provides full cycle accuracy and has excellent debug capabilities, but it runs far too slowly for full verification coverage of both hardware and software in an SoC or a system-of-systems. Testing software may mean first booting an operating system before testing how drivers interact both with the silicon and that operating system. This involves millions of cycles of execution – far more than a simulator can reasonably manage.
  • Hardware prototyping options lack the capacity, debug visibility, and design-change turn-around time necessary for full-chip and system-of-systems verification.
  • New on the scene are new digital-twin representations of vehicles, modeling not just the silicon, but all aspects of a vehicle. These represent an increasingly important component of vehicle verification and validation strategies – and yet they represent a significant computing challenge.

Given the amount of heavy lifting that silicon will perform in vehicles and the enormous cost of having to redo a silicon chip, full verification must be completed before chips are produced, creating what we call a pre-silicon verification gap. Bridging this gap is critical to success in the automotive realm. But that verification must include not just the silicon in isolation, but the ways in which the silicon interacts with the rest of the vehicle.

The paper Emulation Fills the Pre-silicon Verification Gap for Autonomous Vehicles defines the pre-silicon verification gap and explains how to close that gap by:

  • Providing verification applications and resources specific to the automotive domain
  • Giving extensive visibility into the design for efficient debug of hardware and software
  • Ensuring effective communication between all tools in the design and verification flow
  • Allowing a smooth transition between MiL (model-in-loop), SiL (software-in-loop), and pre-silicon HiL (hardware-in-loop)

And much more.

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