High-performance inference runtime and hardware abstraction layer — unifying NPU, GPU, DSP, and other heterogeneous computing resources to deliver extreme compute power for robot AI.
qoocore is the compute acceleration core of the QooBot ecosystem, managing and abstracting various heterogeneous computing hardware to provide a unified acceleration interface for upper-layer applications.
Unified programming interfaces and resource management for NPU, GPU, DSP, FPGA, and other heterogeneous hardware.
Model conversion and performance optimization for TensorFlow, PyTorch, ONNX, and other mainstream frameworks.
Low-latency, high-throughput inference engine supporting batching, pipelining, and dynamic scheduling.
Priority-based scheduling for hard real-time tasks, ensuring instant response for critical computations.
Extensible backend architecture supporting CUDA, OpenCL, Vulkan, Metal, and other compute APIs.
Real-time performance monitoring, power analysis, auto-tuning, and bottleneck diagnosis tools.
qoocore uses a three-layer architecture, from low-level hardware drivers to high-level application interfaces, each optimized for performance and portability.
Support for NVIDIA CUDA, AMD ROCm, Intel oneAPI, and other mainstream GPU compute platforms.
Inference acceleration for Huawei Ascend, Cambricon, Horizon, and other mainstream NPU chips.
Support for Qualcomm Hexagon, Intel FPGA, Xilinx Vitis, and other specialized accelerators.
Cross-framework model conversion, graph optimization, operator fusion, quantization, and compression.
Memory optimization, batching, pipeline parallelism, and dynamic shape support for high-performance inference.
Task scheduling, resource allocation, priority management, and real-time guarantees.
High-performance native C++ interfaces providing fine-grained control and extreme performance.
Python-friendly interfaces with NumPy integration and PyTorch/TensorFlow interoperability.
Remote inference service interfaces supporting cloud deployment and distributed computing.
Near-native inference acceleration across diverse hardware platforms, maximizing hardware utilization.
Unified programming model so applications need not care about underlying hardware details.
Optimized for real-time robotics scenarios, delivering millisecond-level inference latency.
Dynamically adjusts power based on load, balancing performance and energy efficiency to extend robot runtime.
Runtime hardware detection and dynamic loading — switch compute backends without restart.
Complete test suite covering unit, integration, performance, and compatibility testing.
qoocore is a complete chip and acceleration solution welcoming hardware engineers, AI researchers, and system developers to contribute.
Detailed API docs, hardware integration guides, performance optimization manuals, and best practices.
Complete source code, hardware drivers, test frameworks, and build systems.
Active Discord community, hardware discussion groups, and regular tech sharing sessions.
Join Community →qoocore integrates tightly with other QooBot ecosystem projects to build a complete robotics compute stack.
Brain OS, using qoocore as its compute backend for unified AI inference capabilities.
Learn about qoobrain →Hardware reference design, collaborating with qoocore to define compute module hardware interface standards.
Learn about qoobody →Cloud services, providing remote model deployment and distributed computing capabilities for qoocore.
Learn about qoocloud →