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.
