As the demand for bringing down the processing time continues to endure, the companies now focus to optimize the architectural design for their hardware. Although design plays a vital role in increasing the efficiency of operations, there is always something more that software can offer for further enhancing the performance of the overall system. Espressif Systems announces its deep learning library ESP-DL dedicated to optimizing the computations and processing through AI resources.
The deep learning library offers APIs for Neural Network Inference to smoothly transfer and process data for target applications. Some of Espressif’s SoCs may have dominated the market for AI applications but ESP-DL focuses to expand the functionalities and enhance the performance. It also highlights specific support for extensive image processing and mathematical computations.
Some Stats for Espressif’s ESP-DL
According to the official press release by Espressif Systems, the ESP-DL was tested on the ESP32-S3 SoC to verify the assertive high-performance functionality. The test gave a positive result verifying the expected performance evaluation, as a 16-bit detection model was 4.5 times quicker, whereas the face recognition model was 6.25 times quicker. Additionally, the 8-bit face recognition model is 2.5 times quicker than the 16-bit model on ESP32-S3.
Implementation of ESP-DL
The above instance shows the implementation of the ESP-DL library as a project component. The block diagram highlights various parts for making the system perfect for AI applications. Let’s discuss each component briefly:
Platform Conversion for ESP-DL
ESP-DL comes with tools that can assist developers to convert their own model according to the requirements of the application. Developers can now explore various platforms for this conversion using the quantization tool in the platform conversion package. These third-party platforms include TensorFlow, PyTorch, and MXNet for converting the existing model into 8-bit or 16-bit.
Model Zoo is a collection of various inbuilt AI models like Human Face Detection, Human Face Recognition, and Cat Face Detection. The collection reduces the time to build these basic models from scratch and the developers need not reinvent the wheel for deploying some of these basic applications. The company is also working with some more models such as color detection and hand-pose recognition, which will be released soon.
APIs in ESP-DL
ESP-DL provides a significant number of APIs for developers to code their own model, such as Neural Network (NN) for optimizing and fitting the features extracted from the dataset. The APIs are also flexible to build some basic use cases of image processing and matrix operations. Espressif has also given an option for customizing the layers and suggesting feedback with respect to the API functionalities.
Software and Hardware Aspect of ESP-DL
“ESP-DL implements quantized computation and brings about a more efficient kind of software by optimizing the assembly and architecture of the C/C++ code. It is worth mentioning that ESP32-S3, with its vector instructions, high-speed SPI interface, and configurable cache memory, achieves a much faster acceleration in AI applications.”
Developers can now customize their models with the ESP-DL library, thus making it a flexible tool to enhance the neural network inference of the hardware system. You can visit the official GitHub repository to play around with the library.
Saumitra Jagdale is a Backend Developer, Freelance Technical Author, Global AI Ambassador (SwissCognitive), Open-source Contributor in Python projects, Leader of Tensorflow Community India and Passionate AI/ML Enthusiast