Machine Learning and AI Compiler and Frameworks Engineer
Cadence
SAN JOSE
hace 2 días

At Cadence, we hire and develop leaders and innovators who want to make an impact on the world of technology.

Your responsibilities will include :

  • Developing a deep learning compiler stack that takes neural network descriptions (CNNs / RNNs) created in frameworks such as Caffe, PyTorch, TensorFlow, etc.
  • and converts them into code suitable for execution on special-purpose and embedded platforms

  • Developing optimized implementations of a variety of neural-network operations and integrating them into a runtime framework
  • Developing new optimization techniques and algorithms to efficiently map CNNs onto a wide range of Tensilica Xtensa processors and specialized HW
  • Benchmarking end-to-end network performance on a variety of DSP and special-purpose accelerator platforms
  • Enhancing the framework to improve overall functionality and performance on the various hardware platforms
  • Devising multiprocessor / multicore partitioning and scheduling strategies
  • Developing complex programs to validate the functionality and performance of the CNN application programming kit
  • Working with hardware designers to identify opportunities for additional hardware acceleration of neural network functions
  • Working with industry-leading partners and customers to design and standardize neural network APIs
  • Required and desired qualifications :

  • A high level of C and C++ programming expertise with 3-5+ years of experience is required; Python experience highly desired
  • Expertise in software development on Linux and Windows systems including, test, debug and release is required
  • 3-5+ years of experience working on a production compiler is highly desired
  • Prior work with CNNs and familiarity with deep learning frameworks (TensorFlow, Caffe / 2, etc.) is a strong plus
  • Knowledge of and experience with a state-of-the-art compiler stack such as LLVM is highly desired
  • Experience programming and optimizing for embedded platforms such as DSPs with DMA engines highly desired
  • Experience implementing compilation techniques such loop optimization, polyhedral models, and IR construction / transition / lowering techniques is desired
  • Familiarity with the state-of-the-art deep learning compilation approaches (XLA, Glow, etc.) is a plus
  • Familiarity with various deep learning networks and their applications (Classification / Segmentation / Object Detection / RNNs) is a plus
  • Knowledge of neural net exchange formats (ONNX, NNEF) is a plus
  • Familiarity with Android system programming and the Android Neural Network API (NNAPI) is a plus
  • We’re doing work that matters. Help us solve what others can’t.

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