Idiom®
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
MACHINE LEARNING
Computer vision
Natural language processing
Sentiment analysis
Machine translation
Recommendation
COMPUTING
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