Synopsys and SiMa.ai are developing an automotive AI design approach that combines SiMa.ai’s machine-learning accelerator technology and software with Synopsys automotive IP and design tools. The intended users are automakers and Tier 1 suppliers designing chips for advanced driver-assistance systems (ADAS) and in-vehicle infotainment (IVI), not consumers shopping for a finished product.
What is the Synopsys–SiMa.ai collaboration?
The companies first described their automotive collaboration in December 2024. Its aim was workload-specific silicon and software for AI-enabled vehicle features, combining Synopsys electronic design automation (EDA), automotive-grade IP and hardware-assisted verification with SiMa.ai machine-learning accelerator IP and its ML software stack.
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On July 30, 2025, SiMa.ai announced an expanded collaboration focused on chiplet architectures and reference system-on-chip (SoC) designs for ADAS and IVI. On January 6, 2026, SiMa.ai announced the first integrated capability: a blueprint for exploring architectures and beginning virtual software development for next-generation automotive SoCs.
What each company contributes
The integration is intended to bring machine-learning workload modeling and software development into established chip-design and verification workflows. The named Synopsys tools have distinct roles:
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| Tool | Role in the announced approach |
|---|---|
| Platform Architect | Explore architecture options and match machine-learning requirements to an automaker’s workloads. |
| Virtualizer Development Kit (VDK) | Support early software development and testing before physical silicon is available. |
| ZeBu Emulation | Validate pre-silicon power, performance and efficiency estimates through emulation. |
SiMa.ai ML simulators are integrated into Synopsys design platforms. Synopsys also describes combining its electronic digital-twin modeling with SiMa.ai’s ML software stack in a multi-die design approach. The goal is to let customers adapt IP, subsystems, chiplets and complete SoCs to different vehicle platforms.
How it could affect ADAS and in-car infotainment chips
ADAS workloads
The companies identify object detection, lane-keeping assistance, automated parking and collision avoidance as possible ADAS workloads. Synopsys also names automatic emergency braking, adaptive cruise control and driver-monitoring systems. These functions can place demanding real-time processing requirements on vehicle hardware.
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IVI and cockpit workloads
For infotainment and the digital cockpit, examples include voice recognition, gesture control, personalized interfaces and advanced multimedia processing. Synopsys also points to cockpit digital assistants, including generative-AI assistants.
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The intended value is the ability to evaluate hardware and software choices earlier, against the automaker’s actual workloads, before committing to a chip design. That matters for software-defined vehicles, where AI models and software may change over a vehicle’s life. The companies’ announcements describe a design approach, however; they do not establish that a specific vehicle or production chip already uses it.
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- Design: Robust design, easy to maintain
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What performance figures have the companies reported?
SiMa.ai’s July 2025 announcement says ZeBu Emulation power estimates were 95–97% accurate compared with actual silicon. This is a figure reported in the company’s release, not an independently described comparative study.
A Synopsys technical profile quotes SiMa.ai as claiming more than 30 times better compute-power efficiency than “industry alternatives.” The cited material does not give an independent benchmark methodology or enough detail to treat that as a like-for-like comparison across automotive chips. Neither figure establishes a performance outcome for a production vehicle using the collaboration.
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- Equipped with high-performance RK3576 processor, integrated with quad-core Cortex-A72 and quad-core Cortex-A53, providing strong performance and high energy efficiency
- 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
When is the automotive AI IP expected?
In its July 30, 2025 announcement, SiMa.ai said machine-learning accelerator IP and associated software were planned for early-access customers by mid-2026, with production targeted for the end of 2026. It also said a machine-learning IP chiplet combining technology from both companies was planned for mid-2027.
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Can consumers buy a product from the partnership?
No direct retail product is identified in the announcements. This is an enterprise semiconductor-design collaboration for automotive OEMs and Tier 1 suppliers. The cited material provides no consumer product listing, pricing, licensing terms, confirmed customer deployment or independently verified affiliate offer.
For automakers and suppliers assessing the approach, the relevant questions include workload fit, real-time latency, performance per watt, customization, software updateability, pre-silicon validation, functional-safety readiness, development risk and total cost. The available information does not provide neutral head-to-head results on those measures or establish that the announced approach is safety-certified.
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