Introduction
ESP-DL is a lightweight and efficient neural network inference framework designed specifically for ESP series chips. With ESP-DL, you can easily and quickly develop AI applications using Espressif’s System on Chips (SoCs).
Overview
ESP-DL offers APIs to load, debug, and run AI models. The framework is easy to use and can be seamlessly integrated with other Espressif SDKs. ESP-PPQ serves as the quantization tool for ESP-DL, capable of quantizing models from ONNX, Pytorch, and TensorFlow, and exporting them into the ESP-DL standard model format.
ESP-DL Standard Model Format: This format is similar to ONNX but uses FlatBuffers instead of Protobuf, making it more lightweight and supporting zero-copy deserialization, with a file extension of .espdl.
Efficient Operator Implementation: ESP-DL efficiently implements common AI operators such as Conv, Gemm, Add, and Mul. The list of supported operators: operator_support_state.md
Static Memory Planner: The memory planner automatically allocates different layers to the optimal memory location based on the user-specified internal RAM size, ensuring efficient overall running speed while minimizing memory usage.
Dual Core Scheduling: Automatic dual-core scheduling allows computationally intensive operators to fully utilize the dual-core computing power. Currently, Conv2D and DepthwiseConv2D support dual-core scheduling.
8bit LUT Activation: All activation functions except for ReLU and PReLU are implemented using an 8-bit LUT (Look Up Table) method in ESP-DL to accelerate inference. You can use any activation function, and their computational complexity remains the same.
The framework figures below illustrate the overall architecture of ESP-DL.
