Synopsys and SiMa.ai are working together on AI-focused automotive chip designs, combining Synopsys design and verification tools with SiMa.ai machine-learning technology. The collaboration is aimed at helping automakers and suppliers explore and develop systems for advanced driver-assistance systems (ADAS) and in-vehicle infotainment (IVI); it is an enterprise design effort, not a retail chip or vehicle product.
What is the Synopsys and SiMa.ai partnership?
The companies first announced a collaboration in December 2024 to develop workload-specific silicon and software for AI-enabled vehicle features. On July 30, 2025, SiMa.ai announced an expanded effort focused on automotive chiplet architectures and reference system-on-chip (SoC) designs for ADAS and IVI workloads.
The aim is to give automotive original equipment manufacturers (OEMs) and Tier 1 suppliers more ways to co-design hardware and software for software-defined vehicles. In this context, a reference SoC design or architecture blueprint is a starting point for engineering work, not a finished chip that an automaker can install in a production vehicle.
What does each company contribute?
| Company or capability | Role in the collaboration |
|---|---|
| Synopsys | Automotive intellectual property (IP), electronic design automation (EDA) and hardware-assisted verification tools. Its electronic digital-twin modeling is part of the multi-die design approach described by Synopsys. |
| SiMa.ai | Machine-learning accelerator IP, its ML software stack and ML simulators, intended to support AI workloads on automotive platforms. |
| Integrated approach | Use the companies’ technologies to explore and develop customizable IP, subsystems, chiplets and complete SoCs for different vehicle platforms. |
What do Platform Architect, Virtualizer and ZeBu do?
The expanded integration announced in July 2025 names three Synopsys tools. Their roles cover different stages of design rather than three interchangeable ways to run an AI model.
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| Tool | Purpose in the announced workflow |
|---|---|
| Platform Architect | Explore architecture options and match machine-learning requirements to an automaker’s workloads. |
| Virtualizer Development Kit (VDK) | Start software development and testing on a virtual platform before the physical chip is available. |
| ZeBu Emulation | Validate pre-silicon power, performance and efficiency using hardware-assisted emulation. |
SiMa.ai’s ML simulators are integrated into Synopsys design platforms. The intended benefit is to evaluate hardware and software choices earlier, when changing an architecture is generally part of design exploration rather than a change to an already manufactured part. The announcements do not quantify development-time savings or show independent customer results.
Which automotive workloads are in scope?
ADAS
The companies cite object detection, lane-keeping assistance, automated parking and collision avoidance. Synopsys also discusses automatic emergency braking, adaptive cruise control and driver-monitoring systems. These functions can involve time-sensitive processing, but the collaboration announcement does not establish a specific system’s latency, safety certification or production readiness.
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In-vehicle infotainment
Examples include voice recognition, gesture control, personalized user interfaces and advanced multimedia processing. Synopsys also names cockpit digital assistants, including generative-AI assistants. These workloads share a vehicle platform with safety-relevant functions, but the announcements do not specify how a particular design partitions resources between them.
For OEMs and Tier 1 suppliers, a central design challenge is balancing real-time behavior and reliability with tight power and cost constraints. The ability to update vehicle software and AI models over a vehicle’s service life is also part of the stated design context; the announcements do not specify update policies or support periods.
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- Stability: Can be used stably for a long time
- Design: Robust design, easy to maintain
- Easy to install: simple operation, easy to install
- Application Scenario:Widely used in many industrial environments
- Correct use:Correct use can extend the service life of the product
When is the automotive AI IP expected?
SiMa.ai’s July 30, 2025 announcement gave these planned milestones. They are company targets, not confirmation that a release or delivery occurred.
| Milestone | Timing stated in the announcement | Status as of October 3, 2026 |
|---|---|---|
| Machine-learning accelerator IP and associated software for early-access customers | By mid-2026 | The stated target has passed; the available announcement does not confirm whether early access began. |
| Production release of the accelerator IP and associated software | Targeted for the end of 2026 | Still a future target on this date; no confirmed release is established here. |
| Machine-learning IP chiplet integrating technologies from both companies | Planned for mid-2027 | Future planned milestone; no delivery is established here. |
The first integrated capability was announced on January 6, 2026, as a blueprint for architecture exploration and early virtual software development for next-generation automotive SoCs serving ADAS and IVI. That announcement describes a design capability, not the commercial availability of the planned production IP or chiplet.
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- Equipped with 6 TOPS computing power, easy to convert a variety of neural network models based on TensorFlow, MXNet, PyTorch, and Caffe frameworks.
- Supports 4K@120fps (H.265/HEVC, VP9, AVS2, AV1), 4K@60fps (H.264/AVC) decoding and 4K@60fps (H.265/HEVC, H.264/AVC) encoding, easy to deal with HD video tasks
- Different types of traffic can be distributed to different network interfaces: one for external Internet connection and another for internal LAN, which improves security and management flexibility
How should the performance claims be read?
SiMa.ai’s July 2025 release says ZeBu Emulation estimates achieved 95–97% accuracy compared with actual silicon power results. This is a company-reported validation figure; the cited announcement does not provide an independent study or enough test details to treat it as a general accuracy guarantee for every design.
A Synopsys technical profile quotes SiMa.ai as claiming more than 30× better compute-power efficiency than “industry alternatives.” This is a vendor-reported comparison, and the cited material does not provide independent benchmark methodology. It should not be read as a verified head-to-head result for a particular automotive workload.
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Is there a product to buy?
There is no direct retail product established by these announcements. The collaboration concerns semiconductor design tools, IP, software and planned chiplet or SoC development for enterprise customers. Pricing, licensing terms, customer deployments and retail availability are not stated. Any organization evaluating the work would need to confirm current access, schedule and commercial terms directly with the companies.
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