Showing 2 open source projects for "risc"

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    XNNPACK

    XNNPACK

    High-efficiency floating-point neural network inference operators

    XNNPACK is a highly optimized, low-level neural network inference library developed by Google for accelerating deep learning workloads across a variety of hardware architectures, including ARM, x86, WebAssembly, and RISC-V. Rather than serving as a standalone ML framework, XNNPACK provides high-performance computational primitives—such as convolutions, pooling, activation functions, and arithmetic operations—that are integrated into higher-level frameworks like TensorFlow Lite, PyTorch Mobile, ONNX Runtime, TensorFlow.js, and MediaPipe. The library is written in C/C++ and designed for maximum portability, efficiency, and performance, leveraging platform-specific instruction sets (e.g., NEON, AVX, SIMD) for optimized execution. ...
    Downloads: 1 This Week
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  • 2
    Unikraft

    Unikraft

    A next-generation cloud native kernel designed to unlock performance

    Unikraft powers the next generation of cloud-native, containerless applications by enabling you to radically customize and build custom OS/kernels; unlocking best-in-class performance, security primitives, and efficiency savings. Unikraft optimizes resource utilization, leading to smaller footprints (meaning higher server saturation) and improved efficiency in resource-constrained environments. Unikraft is an open-source project driven by a vibrant community of over 100 developers, fostering...
    Downloads: 0 This Week
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