Interfacing with Deep Learning Frameworks

Idiom® interfaces with standard deep learning frameworks and model exchange formats, while providing the transformations and tools required by deep learning model authors and deployers.

Ease of use & workflow

Idiom® does the work when converting a program from your description

  • User selects Envise as their target hardware
  • No change to Pytorch, TensorFlow, or ONNX file necessary

Graph compiler

idCompile automates the programming by partitioning (large) neural networks for parallel programming within and between Envise blades

  • Automatic conversion from floating-point numbers for mixed-precision inference
  • Automatic generation of optimized execution schedule
  • Supports multiple parallelism strategies: data parallelism, model parallelism, and pipelining

Multi-blade Envise partitioning

Idiom® automatically performs the partitioning between multiple Envise blades.

  • Proprietary Lightmatter® fiber optical communication links Envise blades, while Idiom® synchronizes the Envise chips together in a single runtime
  • Automatic partitioning chooses the best parallelism model for performance
  • Virtualizes each Envise blade automatically and multiple users can apportion the number of chips used

Debugging and Profiling

idProfiler provides an in-depth view of the neural network execution over multiple Envise devices

  • Bird’s-eye view of the neural network program including memory usage
  • Identifies bottlenecks and provides information for programmers to optimize their neural network model
  • idBug helps locate errors within the parallel multi-chip program

Idiom® ML Libraries

idML is a complete set of machine learning tools with Pytorch front-end

  • Compresses and quantizes neural networks while maintaining performance
  • Advanced quantization strategies including knowledge distillation and noisy quantization-aware training
  • In-depth and helpful visualization of the neural network performance with different choices of hyperparameters
  • Implements any neural network—from small to large, and from image processing to recommendation models



Computer vision
Natural language processing
Sentiment analysis
Machine translation



High performance computing
Public/private cloud computing
On-premise computing
5G base station computing

  • Automate the deployment of your models to Lightmatter® hardware
  • Optimize your neural network model performance using the Idiom® software stack

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