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aiWare is engineered as a high-performance neural processing unit tailored for automotive AI applications, delivering exceptional power efficiency and computational capability across a broad spectrum of neural network tasks. Its design centers around achieving the utmost efficiency in AI inference, providing flexibility and scalability for various levels of autonomous driving, from basic L2 assistance systems to complex L4 self-driving operations. The aiWare architecture exemplifies leading-edge NPU efficiencies, reaching up to 98% across diverse neural network workloads like CNNs and RNNs, making it a premier choice for AI tasks in the automotive sector. It boasts an industry-leading 1024 TOPS capability, making it suitable for multi-sensor and multi-camera setups required by advanced autonomous vehicle systems. The NPU's hardware determinism aids in achieving high ISO 26262 ASIL B certification standards, ensuring it meets the rigorous safety specifications essential in automotive applications. Incorporating an easy-to-integrate RTL design and a comprehensive SDK, aiWare simplifies system integration and accelerates development timelines for automotive manufacturers. Its highly optimized dataflow and minimal external memory traffic significantly enhance system power economy, providing crucial benefits in reducing operational costs for deployed automotive AI solutions. Vibrant with efficiency, aiWare assures OEMs the capabilities needed to handle modern automotive workloads while maintaining minimal system constraints.
aiData functions as a crucial backbone for automated driving systems, providing a fully automated data pipeline tailored for ADAS and autonomous driving (AD) applications. This pipeline streamlines the Machine Learning Operations (MLOps) workflow, from data collection to curation and annotation, enhancing the development process by minimizing manual intervention. By leveraging AI-driven processes, aiData significantly reduces the resources required for data preparation and validation, making high-quality data more accessible for training sophisticated AI models. One of the key features of aiData is its comprehensive versioning system, which ensures complete transparency and traceability throughout the data lifecycle. This feature is pivotal for maintaining high standards in data quality, allowing developers to track changes and updates efficiently. Furthermore, aiData includes advanced tools for annotating data, supported by AI algorithms, which enable rapid and accurate labeling of both moving and static objects. This capability is particularly beneficial for creating dynamic and contextually-rich datasets needed for training robust AD systems. Beyond data preparation, aiData facilitates seamless integration with existing data infrastructure, supporting both on-premises and cloud-based deployment to cater to varying security and collaboration needs. As automotive companies face growing data requirements, aiData's scalable and modular architecture ensures that it can adapt to evolving project demands, offering invaluable support in the rapid deployment and validation of ADAS technologies.
aiSim 5 represents a leap forward in automotive simulation technology, underpinning the complex validation processes needed for modern autonomous driving systems. Certified to ISO26262 ASIL-D, this simulator is designed to handle the demanding requirements of advanced driver-assistance systems (ADAS) and autonomous driving technologies. By utilizing AI-driven digital twin creation and sophisticated sensor modeling, aiSim ensures high fidelity in simulations, enabling developers to conduct virtual tests across diverse scenarios that replicate real-world conditions. Featuring a physics-based rendering engine, aiSim allows for the precise simulation of varied environmental conditions like rain, fog, and sunshine, as well as complex sensor configurations. Its open architecture and modular design facilitate easy integration into existing development pipelines, ensuring compatibility with a wide range of testing and development frameworks. The simulator's deterministic simulation capabilities provide reliability and repeatability, which are crucial for validating safety-critical automotive functions. The robust architecture of aiSim extends its utility beyond basic simulations, offering tools such as aiFab for scenario randomization, which helps in exposing edge cases that may not be encountered in typical testing environments. Moreover, its ability to produce synthetic data for training improves the robustness of ADAS systems. With aiSim, the development cycle shortens significantly, allowing automotive manufacturers to bring innovative products to market more efficiently.
Discover how Socionext's partnership with aiMotive for aiWare NPU IP is set to transform next-gen automotive ADAS SoC solutions. Read more
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