Create Smarter, More Reliable Software-Defined Vehicles with Synopsys and AWS

Ajit Kolhe, Marc Serughetti, Randy Fish, Stewart Williams, Mohan Yellapantula

Jun 06, 2024 / 5 min read

This article was originally published in AWS for Industries.

Today’s vehicles are increasingly defined by software and innovative semiconductor technology. This increased software content can bring greater risk of failure, whether within the software itself or the semiconductor components. Fortunately, the risks can be mitigated by automakers. The ongoing collaboration between Synopsys and Amazon Web Services (AWS) can help automakers enhance automotive system and system-on-chip (SoC) development for today’s complex software-defined vehicles (SDVs).

Synopsys and AWS are empowering automotive engineers with Synopsys solutions on AWS designed to provide automakers with deeper insights into the health of their chips throughout their entire lifecycles. Additionally, the two companies’ work in this area is also helping automotive engineers validate their complex automotive systems earlier in the design cycle.

The Synopsys solutions involved are:

  • Silicon lifecycle management (SLM) designed to help automakers observe, transport, analyze, and act on data throughout the lifecycle based on embedded monitors in the silicon
  • Virtual prototyping designed for software-in-the-loop (SiL) and virtual hardware-in-the-loop (vHiL) simulations

These Synopsys solutions run on AWS, which provides customers with scalability, security, and elasticity. Read on to learn how Synopsys and AWS, with their complementary technologies, are helping enable automotive OEMs, Tier 1 suppliers, and semiconductor suppliers to create smarter, safer, and more reliable vehicles.

software defined vehicles design

Silicon Lifecycle Management for Predictive Vehicle Maintenance

Silicon chip performance is under increasing pressure due to a host of factors, from process technology variability to environmental conditions and aging effects. SLM technology provides in-chip monitor IPs that collect data from the devices throughout their lifecycles, enabling continuous analysis and actionable feedback. The Synopsys SLM solution powered by AWS is designed to enable the analysis of vehicle data, as selected by the automakers, within the SoC, on the edge, and up to the cloud. SLM uses on-chip IPs, such as power, voltage, and temperature (PVT) monitors, as well as IPs that measure logic, memory, and interconnect timing margins. Automakers select which vehicle data to collect, and data is typically collected and analyzed at every opportunity throughout the lifecycle of silicon devices, enabling actionable insights in-design, in-ramp, in-production, and in-field for optimizing manufacturing yield, improving silicon performance, and estimating the chip’s remaining useful life (Figure 1 outlines this flow). Customers can then choose to have the vehicle send these silicon metrics and events to AWS via a Synopsys SLM edge application for storage and further analysis.

silicon lifecycle management automotive

With a deeper understanding of the health of the silicon devices across a broad population of vehicles, OEMs are better positioned to avoid potential warranty failures and recalls. For example, based on the data, the OEMs may decide to address the flagged issue with an over-the-air (OTA) repair or a service visit for replacement.

Semiconductor suppliers can also benefit from knowing what happens inside their chips in the field, thanks to SLM technology. Chips are designed based on an assumed workload. In the field, however, the mission profile of these devices is changing dramatically. Over the course of their lifecycles, vehicles may run thousands upon thousands of hours in unpredictable environments, such as extreme heat or cold, and often with fluctuating workloads. With deeper insights into how chips perform once inside operational vehicles, semiconductor suppliers can adapt their next chip designs or suggest mitigations for in-field challenges. With silicon health data captured by Synopsys SLM monitors and in-field analytics processing, an automaker could link a specific OTA update version as the cause for a hardware issue. Upon discovering this, the automaker can quickly revert to an earlier firmware version and share insights with the software team to correct the problem before it becomes more widely spread.

How Virtual Prototyping Helps Validate Driving Scenarios

Virtual prototyping is the other area where Synopsys and AWS are collaborating to help enhance the development of SDVs by automakers. Increasing vehicle electrification and reliance on software that runs on hundreds of chips to deliver new vehicle experiences and functions requires fundamental changes in the electrical/electronic (E/E) architecture of vehicles. This, in turn, transforms the software development model to one where it’s critical to:

  • Shift-left, starting software and electronic systems co-development earlier
  • Accelerate the validation of Software Defined Vehicles
  • Enable continuous integration and deployment for seamless OTA software updates by automakers

Thoroughly validating such a complex, integrated system is not feasible for automakers, as it requires automakers to log millions of miles in their test vehicles, evaluating a multitude of scenarios before they hit the market with each software version. While physical road testing still needs to be done, simulations in the virtual world can be started well before a physical prototype of a vehicle even becomes available. Automakers such as Rivian are taking advantage of virtual testing, relying on advanced modeling and simulation technologies running on AWS to test new concepts and quickly bring designs to market.

Considering the high volume of data that virtual testing can generate, the cloud provides an ideal environment for storing, analyzing, and delivering secure access to the information more cost-effectively than hosting data on on-prem servers. After the automaker has conducted extensive virtual testing, real-world testing by the automaker can validate the simulations of various driving scenarios, including an array of environmental conditions and different driving distractions.

Electronics Digital Twins Drive Greater Productivity

Virtual prototyping helps enable electronics digital twins, which are digital replicas of electronics systems, hardware, software, and environment. This can be a digital representation of an SoC, electronic control unit (ECU), or even a full vehicle electrical and electronic system. Electronics digital twins bring greater productivity and system quality, as well as reduced costs, compared to relying solely on a physical prototype. They can be used throughout a product’s lifecycle for:

  • Software development and regression testing in the context of hardware
  • Performance validation to better understand whether the system being built will perform as intended in the end application
  • Power analysis to determine whether the system will deliver the optimal power/performance mix in the end application when running heavy workloads
  • Validation of OTA software updates or vehicle subsystems

To help OEMs deploy digital twins, Synopsys and AWS provide a comprehensive set of virtual prototyping technologies, cloud services, and deep automotive expertise. Customers using these technologies can start their development process earlier and run thousands of tests in parallel, as opposed to running a much smaller number of tests sequentially on a limited number of physical testbenches.


Technologies such as cloud-based silicon health monitoring and electronics digital twin platforms can help deliver the productivity, time to market, and quality boost that OEMs, Tier 1s, and semiconductor companies need to be competitive and improve the customer experience. Through cloud-based SLM and virtual prototyping technologies, Synopsys and AWS help provide the foundation for automaker’s to further advance the development of their SDVs. To learn more, watch the webinar, “Leveraging Electronics Digital Twins on AWS to Help Accelerate Software-Defined Vehicle Validation.

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