SiMa.ai, Synopsys Target Faster Automotive AI Chip Development

As automotive software grows more complex and AI workloads move deeper into vehicles, chip design has emerged as a critical bottleneck. Automakers are under pressure to deliver advanced driver assistance and intelligent in-cabin experiences faster, without absorbing runaway development costs or production risks.

SiMa.ai and Synopsys believe the answer lies earlier in the design cycle.

The two companies announced the first integrated capability from their strategic collaboration, offering a blueprint aimed at accelerating architecture exploration and early software development for AI-ready automotive system-on-chips (SoCs). The joint solution targets next-generation platforms supporting Advanced Driver Assistance Systems (ADAS) and In-Vehicle Infotainment (IVI).

Why Automotive AI Can’t Wait For Silicon Anymore

Modern vehicles increasingly resemble data centers on wheels. AI models for perception, decision-making, and in-cabin intelligence demand compute platforms that are power-efficient, workload-aware, and validated long before silicon arrives.

The SiMa.ai–Synopsys blueprint is designed to help automakers “shift left” software development—starting validation and optimization at the virtual prototype stage rather than waiting for physical chips. The companies say this approach can reduce development risk, improve software quality, and shorten time to start of production.

At the core of the collaboration is a shared focus on machine-learning-optimised SoC architectures that can scale with rising autonomy levels while staying within automotive power and cost constraints.

A Practical Path From Architecture To Software

Rather than positioning the blueprint as a single tool, the companies are offering a pre-integrated workflow that spans early architecture decisions through pre-silicon validation.

For early-stage exploration, automotive teams can use the SiMa.ai MLA Performance and Power Estimator (MPPE) tool to evaluate different ML accelerator configurations against real workloads. This allows teams to right-size designs instead of overbuilding compute capacity.

That process is complemented by Synopsys Platform Architect, which models system-level trade-offs across performance, power, memory, and interconnects before RTL design begins, an increasingly critical step as SoCs grow more heterogeneous.

Starting Software Before Hardware Exists

One of the more significant implications of the blueprint lies in early software enablement.

Using Synopsys’ Virtualizer Development Kit (VDK), automakers can begin software development on a virtual SoC prototype well before silicon is available. According to the companies, this can enable full system bring-up within days of silicon readiness and potentially accelerate vehicle programs by up to 12 months.

On the AI side, SiMa.ai’s Palette SDK supports deployment of complex edge AI applications across different ML workflows, allowing teams to develop, test, and optimize models without compromising performance.

For deeper validation, Synopsys ZeBu emulation enables pre-silicon hardware-software verification, helping teams assess performance and power behavior against expected automotive workloads.

Industry Context: Software-Defined Vehicles Need Predictability

Automotive OEMs are facing a fundamental shift. Vehicles are now defined by software update cycles, not just hardware refreshes. That makes predictability in compute platforms a business requirement, not just an engineering goal.

“We are pleased with how well the two teams have worked together to quickly create a joint solution uniquely focused on unlocking physical AI capabilities for today's software-defined vehicles,” said Krishna Rangasayee, Founder & CEO, SiMa.ai. “Our best-in-class ML platform, combined with Synopsys' industry-leading automotive-grade IP and design automation software, creates a powerful foundation for innovation across OEMs in autonomous driving and in-vehicle experiences.”

From Synopsys’ perspective, early validation is no longer optional.

“Automotive OEMs need to deliver software-defined AI-enabled vehicles faster to market to drive differentiation, which requires early power optimisation and validation of the compute platform,” said Ravi Subramanian, Chief Product Management Officer, Synopsys.

Reducing Risk Before It Reaches The Factory Floor

The collaboration reflects a broader shift in automotive silicon development. As AI workloads grow more dynamic, the cost of late-stage design changes becomes harder to absorb—both financially and operationally.

By enabling architecture exploration, software development, and validation earlier in the lifecycle, SiMa.ai and Synopsys are positioning their blueprint as a way to de-risk programmes before they reach manufacturing.

For automakers balancing autonomy ambitions with production realities, the message is clear: the future of automotive AI will be decided well before the first chip rolls off the line.