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The Chimera GPNPU from Quadric is engineered to meet the diverse needs of modern AI applications, bridging the gap between traditional processing and advanced AI model requirements. It's a fully licensable processor, designed to deliver high AI inference performance while eliminating the complexity of traditional multi-core systems. The GPNPU boasts an exceptional ability to execute various AI models, including classical backbones, state-of-the-art transformers, and large language models, all within a single execution pipeline.\n\nOne of the core strengths of the Chimera GPNPU is its unified architecture that integrates matrix, vector, and scalar processing capabilities. This singular design approach allows developers to manage complex tasks such as AI inference and data-parallel processing without resorting to multiple tools or artificial partitioning between processors. Users can expect heightened productivity thanks to its modeless operation, which is fully programmable and efficiently executes C++ code alongside AI graph code.\n\nIn terms of versatility and application potential, the Chimera GPNPU is adaptable across different market segments. It's available in various configurations to suit specific performance needs, from single-core designs to multi-core clusters capable of delivering up to 864 TOPS. This scalability, combined with future-proof programmability, ensures that the Chimera GPNPU not only addresses current AI challenges but also accommodates the ever-evolving landscape of cognitive computing requirements.
Quadric's Chimera SDK is a robust development environment aimed at simplifying the process of developing applications for the Chimera processor. This Software Development Kit comprises a comprehensive set of tools designed to facilitate efficient mixing and matching of machine learning algorithms and C++ code.\n\nThe SDK features the Chimera Graph Compiler, which translates AI models into optimized C++ code, ready for deployment on Chimera cores. Developers benefit from seamless integration of AI graphs from frameworks like TensorFlow and PyTorch, without the need for extensive pre-processing or operator substitution. This process is supported by the LLVM C++ compiler, ensuring a smooth workflow from model development to execution.\n\nMoreover, the Chimera SDK offers extensive profiling capabilities, allowing developers to fine-tune performance and resource allocation. This includes detailed cycle count analysis, data bandwidth usage evaluations, and power profiling, thereby streamlining the optimization of applications running on Chimera processors. This toolkit not only aids in rapid development but also enhances the performance and efficiency of AI models deployed on these processors.
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