MulticoreWare

中文

中文

中文

中文

Framework-Flexible Custom Operators

Framework-Flexible Custom Operators
Synergizing ML Frameworks with Cloud Solutions for Scalable AI

Software Stack

MulticoreWare showcases strong expertise in runtime environments like ONNX and TensorFlow Lite (TFLite), as well as Android Neural Networks (NN) frameworks. We provide solutions for optimizing and accelerating both machine learning model inference and training, ensuring efficient execution across various hardware platforms.

AI / ML Accelerators

MulticoreWare’s support for Android NN, specifically the Neural Networks API (NNAPI), enables the efficient execution of machine learning operations on Android devices through dedicated AI hardware accelerators, enhancing overall performance and responsiveness. Our proficiency in ONNX and TFLite runtimes allows for seamless deployment and interoperability of models.

MulticoreWare possesses extensive experience in developing Android NN drivers and implementing model inference offloading to specific AI accelerators. Our expertise extends to integrating customer-specific AI engine backends into the runtime, resulting in optimized pipelines for both floating-point and quantized models.

This capability enables seamless execution of machine learning workloads on Android devices while harnessing the power of dedicated AI accelerators. MulticoreWare’s proficiency in custom integration, optimization, and hardware acceleration empowers efficient and high-performance AI deployments in mobile environments.

Modern C++ Library Usage

The tasks conducted by MulticoreWare in TensorFlow and PyTorch primarily rely on the utilization of C++14/17. Our approach emphasizes template-based flexibility over traditional class/interface structures, enabling adaptable and efficient code design. This coding strategy enhances code reliability, performance, and maintainability in our projects. Furthermore, this approach ensures automatic resource management through shared unique pointers.

MulticoreWare’s utilization of standard library containers and algorithms signifies our commitment to established and optimized coding practices. The comprehensive approach to STL, error handling, metaprogramming, and library utilization underscores our teams’ dedication to producing robust, efficient, and maintainable software solutions.

Our engineers are skilled in creating a modular and adaptable pipeline for layer implementation. This design strategy allows for the seamless integration of different layers in the pipeline, promoting code reusability and maintainability. Kernel switching enables efficient runtime selection of optimized computation kernels, contributing to enhanced performance and tailored hardware utilization.

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