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Chimera GPNPU is engineered to revolutionize AI/ML computational capabilities on single-core architectures. It efficiently handles matrix, vector, and scalar code, unifying AI inference and traditional C++ processing under one roof. By alleviating the need for partitioning AI workloads between different processors, it streamlines software development and drastically speeds up AI model adaptation and integration. Ideal for SoC designs, the Chimera GPNPU champions an architecture that is both versatile and powerful, handling complex parallel workloads with a single unified binary. This configuration not only boosts software developer productivity but also ensures an enduring flexibility capable of accommodating novel AI model architectures on the horizon. The architectural fabric of the Chimera GPNPU seamlessly blends the high matrix performance of NPUs with C++ programmability found in traditional processors. This core is delivered in a synthesizable RTL form, with scalability options ranging from a single-core to multi-cluster designs to meet various performance benchmarks. As a testament to its adaptability, the Chimera GPNPU can run any AI/ML graph from numerous high-demand application areas such as automotive, mobile, and home digital appliances. Developers seeking optimization in inference performance will find the Chimera GPNPU a pivotal tool in maintaining cutting-edge product offerings. With its focus on simplifying hardware design, optimizing power consumption, and enhancing programmer ease, this processor ensures a sustainable and efficient path for future AI/ML developments.
The Chimera SDK is an advanced software suite designed to enhance the development and deployment of AI models on Quadric's Chimera processors. It provides a complete environment where developers can simulate, profile, and deploy applications using a code-driven approach that embraces both traditional C++ and modern machine learning models. This SDK simplifies the AI model preparation for the Chimera GPNPU by providing tool-driven processes that require no network adjustments or operator substitutions. The Quadric Chimera SDK is adept at transforming ONNX graphs derived from popular training platforms like TensorFlow and PyTorch into optimized C++ code that can be directly executed on Chimera processors. By integrating graph compiler capabilities and LLVM-based code optimization, the SDK empowers developers to fully exploit the hardware capabilities of Chimera, ensuring efficient and streamlined execution of diverse AI workloads. This toolkit supports deployment on private cloud or on-premise systems, further enhancing its flexibility for varied developmental needs.
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