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Quadric's Chimera GPNPU is an adaptable processor core designed to respond efficiently to the demand for AI-driven computations across multiple application domains. Offering up to 864 TOPS, this licensable core seamlessly integrates into system-on-chip designs needing robust inference performance. By maintaining compatibility with all forms of AI models, including cutting-edge large language models and vision transformers, it ensures long-term viability and adaptability to emerging AI methodologies. Unlike conventional architectures, the Chimera GPNPU excels by permitting complete workload management within a singular execution environment, which is vital in avoiding the cumbersome and resource-intensive partitioning of tasks seen in heterogeneous processor setups. By facilitating a unified execution of matrix, vector, and control code, the Chimera platform elevates software development ease, and substantially improves code maintainability and debugging processes. In addition to high adaptability, the Chimera GPNPU capitalizes on Quadric's proprietary Compiler infrastructure, which allows developers to transition rapidly from model conception to execution. It transforms AI workflows by optimizing memory utilization and minimizing power expenditure through smart data storage strategies. As AI models grow increasingly complex, the Chimera GPNPU stands out for its foresight and capability to unify AI and DSP tasks under one adaptable and programmable platform.
The Chimera SDK from Quadric offers a comprehensive suite of tools designed specifically for developers working on complex AI and machine learning projects. It provides the necessary infrastructure to not only develop but also simulate, profile, and optimize applications for Quadric's Chimera processors. A noteworthy feature of the SDK is its compatibility with commonly used AI frameworks, seamlessly converting ONNX models into optimized C++ code capable of running efficiently on the Chimera hardware. Incorporating the Chimera Graph Compiler (CGC), the SDK allows developers to undertake graph conversion by performing multiple optimization processes, such as graph simplification and constant propagation. This creates an environment where machine learning inference models are automatically refined and prepared for efficient execution on the hardware. Furthermore, the SDK provides extended capabilities for local and remote deployment, supporting a private cloud or on-premises setups. By streamlining the development process, the Chimera SDK delivers an easy-to-use interface for compiling, optimizing, and running extensive AI models, offering developers the power and flexibility to adapt and optimize new algorithms swiftly.
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