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The Chimera GPNPU by Quadric is a versatile processor specifically designed to enhance machine learning inference tasks on a broad range of devices. It provides a seamless blend of traditional digital signal processing (DSP) and neural processing unit (NPU) capabilities, which allow it to handle complex ML networks alongside conventional C++ code. Designed with a focus on adaptability, the Chimera GPNPU architecture enables easy porting of various models and software application programming, making it a robust solution for rapidly evolving AI technologies. A key feature of the Chimera GPNPU is its scalable design, which extends from 1 to a remarkable 864 TOPs, catering to applications from standard to advanced high-performance requirements. This scalability is coupled with its ability to support a broad range of ML networks, such as classic backbones, vision transformers, and large language models, fulfilling various computational needs across industries. The Chimera GPNPU also excels in automotive applications, including ADAS and ECU systems, due to its ASIL-ready design. The processor's hybrid architecture merges Von Neumann and 2D SIMD matrix capabilities, promoting efficient execution of scalar, vector, and matrix operations. It boasts a deterministic execution pipeline and extensive customization options, including configurable instruction caches and local register memories that optimize memory usage and power efficiency. This design effectively reduces off-chip memory accesses, ensuring high performance while minimizing power consumption.
The Chimera SDK from Quadric is an all-encompassing software development toolkit designed to facilitate the creation and optimization of complex applications for the Chimera GPNPU. It offers developers a powerful platform to blend data parallel algorithms expressed as machine learning graphs with traditional C++ code, thereby delivering unprecedented flexibility and performance. This toolkit provides users with access to Quadric's Developer Studio, where applications can be developed using a straightforward drag-and-drop interface. The Chimera SDK supports both online and on-premise use, allowing developers to either work directly within a browser environment or download and deploy the SDK within their own infrastructure. This flexibility ensures that proprietary C++ code and ML models can be compiled and optimized efficiently. The SDK is bolstered by the Chimera Graph Compiler, which transforms ML inference models from popular training frameworks into optimized C++ code. This streamlined conversion process, alongside the detailed profiling capabilities of the Chimera Instruction Set Simulator, enables developers to thoroughly optimize their applications for both performance and memory utilization. The Chimera SDK thus represents a significant leap forward in simplifying the design and deployment of sophisticated AI applications.
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