Synopsys, a leading provider of silicon and software solutions, has announced a strategic collaboration with SiMa.ai, a machine learning system-on-chip (MLSoC) company. This partnership aims to accelerate the development of AI-enabled silicon and software for next-generation vehicles, specifically addressing the demands of Advanced Driver Assistance Systems (ADAS) and In-Vehicle Infotainment (IVI) applications. The joint solution will combine Synopsys’ electronic design automation (EDA), automotive-grade IP, and hardware verification technologies with SiMa.ai’s machine learning accelerator (MLA) IP and ML software stack. This collaboration will help automakers deliver power-efficient solutions for workload-specific chip development, with a focus on early architecture exploration, optimized software development, and cost-effective in-vehicle experiences. The solution will also support continuous upgrades, including over-the-air updates for automotive edge AI applications, and provide guidance for automotive design engineers in choosing performance, power, and software application requirements for custom or third-party SoC development. Krishna Rangasayee, founder and CEO of SiMa.ai, announced the collaboration on LinkedIn, stating that it will help automotive OEMs and Tier1s globally with their ADAS and IVI needs. Ravi Subramanian, head of Synopsys’s product management and markets group, added that the collaboration will modernize hardware/software co-design processes and enable customers to meet cost considerations and industry standards. SiMa.ai’s MLSoC platform delivers high performance at low power, making it an ideal solution for creating smarter and safer vehicles. In September, SiMa.ai launched MLSoC Modalix, the industry’s first multi-modal edge AI product family, to address the increasing demand for generative AI and real-time, multi-modal in-car experiences. This partnership comes at a time when automotive manufacturers are under pressure to integrate software and hardware while meeting strict performance, reliability, and cost requirements. The fully customizable workflow offered by both companies will reduce development costs and support faster time-to-market for AI-enabled automotive solutions.
