esp-dl

espressif/esp-dl is ranked 3,939 among these 8 peers; lmstudio-ai/mlx-engine is ranked 3,932.

md) 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 . **Flexible Mixed Quantization**: ESP-DL supports 8-bit (w8a8), 16-bit (w16a16), and mixed-precision (w8a16) quantization. These schemes can be freely combined within a model to preserve accuracy while maximizing inference efficiency. **Efficient Operator Implementation**: ESP-DL

efficiently implements common AI operators such as Conv, Gemm, Add, and Mul. For performance benchmark data of some operators on ESP32-S3 and ESP32-P4, please refer to benchmark_report. **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. News [2026/9/1] Starting from ESP-DL v3. 11, w8a16 mixed quantization is fully supported. [2026/7/30] Added PP-OCRv6 OCR model and example, recognizing Chinese, English and

46 Latin-script languages on-device on ESP32-P4. [2026/6/5] Introduce two skills, esp32s3-pie-simd and esp32p4-pie-simd, to help the code agent rewrite the code into a PIE-accelerated version. [2026/5/13] Introduced **espdl-quantize skill**, which automatically iterates to search for optimal esp-ppq quantization strategies, reducing manual tuning costs and improving quantized model accuracy. [2026/4/23] Support model quantization via AutoQuant tool, which automatically explores optimal quantization strategies and parameters, significantly

reducing manual tuning effort. [2026/4/20] Conv and Gemm operators on ESP32-P4 have been upgraded to per-channel quantization, significantly improving model accuracy. The new ESP-DL maintains backward compatibility, and existing per-tensor models are not affected. [2026/3/12] YOLO26 model has been added, thanks to BoumedineBillal for the contribution. Start training and deploying your own YOLO26 model. [2026/3/11] The ESP-DL Agent Tool has been evolved into **espdl-operator skill**, making it easier

for AI Agents to automatically implement operators. See how_to_implement_operators_with_ai_agent. [2026/2/27] Support quantizing