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The Chimera GPNPU from Quadric is designed as a general-purpose neural processing unit intended to meet a broad range of demands in machine learning inference applications. It is engineered to perform both matrix and vector operations along with scalar code within a single execution pipeline, which offers significant flexibility and efficiency across various computational tasks. This product achieves up to 864 Tera Operations per Second (TOPs), making it suitable for intensive applications including automotive safety systems. Notably, the GPNPU simplifies system-on-chip (SoC) hardware integration by consolidating hardware functions into one processor core. This unification reduces complexity in system design tasks, enhances memory usage profiling, and optimizes power consumption when compared to systems involving multiple heterogeneous cores such as NPUs and DSPs. Additionally, its single-core setup enables developers to efficiently compile and execute diverse workloads, improving performance tuning and reducing development time. The architecture of the Chimera GPNPU supports state-of-the-art models with its Forward Programming Interface that facilitates easy adaptation to changes, allowing support for new network models and neural network operators. It’s an ideal solution for products requiring a mix of traditional digital signal processing and AI inference like radar and lidar signal processing, showcasing a rare blend of programming simplicity and long-term flexibility. This capability future-proofs devices, expanding their lifespan significantly in a rapidly evolving tech landscape.
The Chimera Software Development Kit (SDK) by Quadric empowers developers with a robust platform to create and deploy complex AI and machine learning applications efficiently. It offers tools for developing, simulating, profiling, and deploying software, perfectly adaptable for Quadric’s Chimera GPNPU. The SDK simplifies coding by allowing integration of machine learning graph code with traditional C++ code into a singular, streamlined programming flow. This SDK includes the Chimera LLVM C++ compiler which utilizes state-of-the-art compiler infrastructure tailored to Chimera's specific instruction sets, driving efficiency and optimization. The SDK is compatible with Docker environments, enabling seamless on-premises or cloud-based deployment. This flexibility supports the versatile development needs of corporates working with private proprietary models while streamlining the toolchain for increased productivity. Its Graph Compiler transcodes machine learning inference models from popular frameworks like TensorFlow and PyTorch into optimized C++ using the Chimera Compute Library. This feature ensures that even the most complex AI models are efficiently deployed, lowering computational overheads and maximizing processing potential. Hence, the Chimera SDK serves as an invaluable tool for engineers aiming to expedite the deployment of cutting-edge ML algorithms both effectively and swiftly.
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