Efficiera SDK

Efficiera comes with a comprehensive AI software suite that allows you to compile AI models and implement AI applications in production environments

Software Development Kit Optimized for Quantized Models, Bringing AI to Practical Use

For using Efficiera, software plays a pivotal role. Addressing the quantization of neural network becomes a critical concern.
However, most neural network frameworks do not support extremely low bit quantization by default. Hence, it is necessary to implement a quantizer for the neural network
It also requires implementing a proprietary AI model compiler for Efficiera, and designing its own network architecture to maximize the performance. For Efficiera, the software is as important as the hardware.
LeapMind provides a comprehensive software suite that automatically optimizes these processes.

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FEATURES

Efficiera NDK

Model Building & Training

  • Operator library for Extremely low bit quantization model

  • PyTorch support

Efficiera Converter

Model Conversion & Optimization

  • Converted to Efficiera executable instruction format

  • Memory bandwidth optimization

  • Parallelization of data transfers and operations

  • Provides performance profiling tools

Efficiera Runtime Library

Deploy and Run

  • Runtime API（Python, C++）

  • Run state management of multiple models

  • Provides a simulator environment

WORKFLOW

Can be scrolled horizontally.

FUNCTIONS

Efficiera NDK

Feature

  • The only environment capable of developing deep learning models for Extremely low bit quantization

  • Libraries for deep learning frameworks, defining operators that can be executed by Efficiera and Runtime Library

  • Models can be built without special attention to the operators required for learning the quantization and scaling coefficients, which is a feature of Extremely low bit quantization.

  • Utilize PyTorch functionality, such as visualization with TensorBoard, without modification

  • Export study results in ONNX format

Supported Frameworks

  • PyTorch

Operating environment

  • Ubuntu 18.04 or 20.04 environment with CUDA-enabled GPU

Efficiera Converter

Feature

  • The only tool capable of converting an Extremely low bit quantized deep learning model into an instruction for Efficiera

  • Convert ONNX format learned and exported using Efficiera NDK into a sequence of instructions that Efficiera IP can execute

  • Advanced optimization to meet resource and memory bandwidth constraints for specified Efficiera configurations

    • High speed by parallel execution of data transfer and arithmetic operations

    • Power saving by reduced memory access

  • Estimate the number of execution cycles, memory bandwidth, and memory usage for a given Efficiera configuration

    • Efficiera configurations can be selected to suit the size and structure of the model

Operating environment

  • Ubuntu 18.04 environment for Intel 64/AMD64 processors

Efficiera Runtime

Feature

  • Runtime library for applications that maximize the performance of Efficiera-equipped hardware

  • Provides API to execute the instruction sequence output from Efficiera Converter on the target

  • Capable of managing correspondence between multiple deep learning models, Efficiera instances, and CPU threads

  • Accelerated by CPU SIMD instructions when available

  • Define APIs for C++ and Python

  • Simulators on the host environment are also provided, allowing application development even when the target is not yet complete.

Operating environment

  • Target (actual device): Ubuntu environment for Armv8-A AArch64 and Armv7-A processors

  • Host: Ubuntu 18.04 environment for Intel 64/AMD64 processors