Diwa: Tiny AI/ML Library v0.0.8

Diwa: Tiny AI/ML Library

Diwa is a lightweight library providing a simple implementation of Feedforward Artificial Neural Networks (ANNs) for microcontrollers such as ESP32, ESP8266, RP2040, and similar development boards (specially boards with PSRAM); it is also compatible for desktop environments (Windows, MacOS, and Linux-based OSes), WebAssembly, and even PSP gaming consoles. It is designed for resource-constrained environments but can be used with non-Arduino platform projects, offering a streamlined solution for tasks that require neural network capabilities.

Diwa stands out as a straightforward and effective solution for implementing artificial neural networks on microcontrollers. Key features include:

• Lightweight: Designed for resource-constrained microcontroller environments yet can still be used within non-Arduino environments.
• Simple Implementation: Provides a basic yet effective implementation of a Feedforward ANN.
• Easy Integration: Suitable for microcontrollers like ESP8266, ESP32, and RP2040. Also compatible with desktop environments, WebAssembly, and even PSP gaming console.
• Training Support: Includes methods for training the neural network using backpropagation.

Note

Diwa is primarily intended for lightweight applications. For more intricate tasks, consider using advanced machine learning libraries on more powerful platforms.

See live demo on Wokwi.

Architecture/Platform Support

Diwa are tested on the following architecture/platform:

Arch/Platform	Remarks
✅ ESP32-WROOM ✅ ESP32-WROVER	NodeMCU DevKit (Automatically using PSRAM available)
✅ ESP8266	Wokwi Emulation
✅ RP2040	Raspberry Pi Pico (RP2040)
🔼 PSP	PPSSPP Emulator (Diwa::loadFromFile and Diwa::saveToFile not yet supported)
✅ Desktop Environments	Works perfectly on Windows, MacOS, and Linux.
✅ WebAssembly (WASM)	Tested via Emscripten

Full Changelog: v0.0.7...v0.0.8

Diwa: Tiny AI/ML Library v0.0.7

Diwa: Tiny AI/ML Library

Diwa is a lightweight library providing a simple implementation of Feedforward Artificial Neural Networks (ANNs) for microcontrollers such as ESP32, ESP8266, RP2040, and similar development boards (specially boards with PSRAM); it is also compatible for desktop environments (Windows, MacOS, and Linux-based OSes), WebAssembly, and even PSP gaming consoles. It is designed for resource-constrained environments but can be used with non-Arduino platform projects, offering a streamlined solution for tasks that require neural network capabilities.

Diwa stands out as a straightforward and effective solution for implementing artificial neural networks on microcontrollers. Key features include:

• Lightweight: Designed for resource-constrained microcontroller environments yet can still be used within non-Arduino environments.
• Simple Implementation: Provides a basic yet effective implementation of a Feedforward ANN.
• Easy Integration: Suitable for microcontrollers like ESP8266, ESP32, and RP2040. Also compatible with desktop environments, WebAssembly, and even PSP gaming console.
• Training Support: Includes methods for training the neural network using backpropagation.

Note

Diwa is primarily intended for lightweight applications. For more intricate tasks, consider using advanced machine learning libraries on more powerful platforms.

See live demo on Wokwi.

Architecture/Platform Support

Diwa are tested on the following architecture/platform:

Arch/Platform	Remarks
✅ ESP32-WROOM ✅ ESP32-WROVER	NodeMCU DevKit (Automatically using PSRAM available on WROVER)
✅ ESP8266	Wokwi Emulation
✅ RP2040	Raspberry Pi Pico (Zero)
🔼 PSP	PPSSPP Emulator (Diwa::loadFromFile and Diwa::saveToFile not yet supported)
✅ Desktop Environments	Works perfectly on Windows, MacOS, and Linux. Segmentation fault on CI/CD.
✅ WebAssembly (WASM)	Tested via Emscripten

Full Changelog: v0.0.6...v0.0.7

Diwa: Tiny AI/ML Library v0.0.6

Diwa: Arduino Tiny AI/ML Library

Diwa is a lightweight library providing a simple implementation of Feedforward Artificial Neural Networks (ANNs) for microcontrollers such as ESP8266, ESP32, RP2040, and similar development boards (specially boards with PSRAM); it is also compatible for desktop environments (tested only on Windows OS) and even PSP gaming console. It is designed for resource-constrained environments but can be used with non-Arduino platform projects, offering a streamlined solution for tasks that require neural network capabilities.

Diwa stands out as a straightforward and effective solution for implementing artificial neural networks on microcontrollers. Key features include:

• Lightweight: Designed for resource-constrained microcontroller environments yet can still be used within non-Arduino environments.
• Simple Implementation: Provides a basic yet effective implementation of a Feedforward ANN.
• Easy Integration: Suitable for microcontrollers like ESP8266, ESP32, and RP2040. Also compatible with desktop environments and even PSP gaming console.
• Training Support: Includes methods for training the neural network using backpropagation.

Note

Diwa is primarily intended for lightweight applications. For more intricate tasks, consider using advanced machine learning libraries on more powerful platforms.

See live demo on Wokwi.

Architecture/Platform Support

Diwa are tested on the following architecture/platform:

Arch/Platform	Remarks
✅ ESP32-WROOM ✅ ESP32-WROVER	NodeMCU DevKit
✅ ESP8266	Wokwi Emulation
✅ RP2040	Raspberry Pi Zero
✅ PSP	PPSSPP Emulator
🔼 AMD64	Works on Windows, segmentation fault on Linux systems

Full Changelog: v0.0.5...v0.0.6

Diwa: Tiny AI/ML Library v0.0.5

Diwa: Arduino Tiny AI/ML Library

Diwa is a lightweight library providing a simple implementation of Feedforward Artificial Neural Networks (ANNs) for microcontrollers such as ESP8266, ESP32, RP2040, and similar development boards (specially boards with PSRAM). It is designed for resource-constrained environments but can be used with non-Arduino platform projects, offering a streamlined solution for tasks that require neural network capabilities.

Diwa stands out as a straightforward and effective solution for implementing artificial neural networks on microcontrollers. Key features include:

• Lightweight: Designed for resource-constrained microcontroller environments yet can still be used within non-Arduino environments.
• Simple Implementation: Provides a basic yet effective implementation of a Feedforward ANN.
• Easy Integration: Suitable for microcontrollers like ESP8266, ESP32, and RP2040.
• Training Support: Includes methods for training the neural network using backpropagation.

Note

Diwa is primarily intended for lightweight applications. For more intricate tasks, consider using advanced machine learning libraries on more powerful platforms.

See live demo on Wokwi.

Architecture/Platform Support

Diwa are tested on the following architecture/platform:

Arch/Platform	Remarks
✅ ESP32	NodeMCU DevKit (Both WROOM and WROVER)
✅ ESP8266	Wokwi Emulation
✅ RP2040	Raspberry Pi Zero
🔼 AMD64	Works on Windows, segmentation fault on Linux systems

Full Changelog: v0.0.4...v0.0.5

Diwa: Tiny AI/ML Library v0.0.4

Diwa: Arduino Tiny AI/ML Library

Diwa is a lightweight library providing a simple implementation of Feedforward Artificial Neural Networks (ANNs) for microcontrollers such as ESP8266, ESP32, RP2040, and similar development boards (specially boards with PSRAM). It is designed for resource-constrained environments but can be used with non-Arduino platform projects, offering a streamlined solution for tasks that require neural network capabilities.

Diwa stands out as a straightforward and effective solution for implementing artificial neural networks on microcontrollers. Key features include:

• Lightweight: Designed for resource-constrained microcontroller environments yet can still be used within non-Arduino environments.
• Simple Implementation: Provides a basic yet effective implementation of a Feedforward ANN.
• Easy Integration: Suitable for microcontrollers like ESP8266, ESP32, and RP2040.
• Training Support: Includes methods for training the neural network using backpropagation.

Note

Diwa is primarily intended for lightweight applications. For more intricate tasks, consider using advanced machine learning libraries on more powerful platforms.

See live demo on Wokwi.

Full Changelog: v0.0.2...v0.0.4

Diwa: Tiny AI/ML Library v0.0.3

Diwa: Arduino Tiny AI/ML Library

Diwa is a lightweight library providing a simple implementation of Feedforward Artificial Neural Networks (ANNs) for microcontrollers such as ESP32 and similar development boards that has PSRAM with Arduino platform. It is designed for resource-constrained environments but can be used with non-Arduino platform projects, offering a streamlined solution for tasks that require neural network capabilities.

Diwa stands out as a straightforward and effective solution for implementing artificial neural networks on microcontrollers. Key features include:

• Lightweight: Designed for resource-constrained microcontroller environments yet can still be used within non-Arduino environments.
• Simple Implementation: Provides a basic yet effective implementation of a Feedforward ANN.
• Easy Integration: Suitable for microcontrollers like ESP32 and similar boards with PSRAM.
• Training Support: Includes methods for training the neural network using backpropagation.

Note

Diwa is primarily intended for lightweight applications. For more intricate tasks, consider using advanced machine learning libraries on more powerful platforms.

See live demo on Wokwi.

Full Changelog: v0.0.2...v0.0.3

Diwa: Tiny AI/ML Library v0.0.2

Diwa: Arduino Tiny AI/ML Library

Diwa is a lightweight library providing a simple implementation of Feedforward Artificial Neural Networks (ANNs) for microcontrollers such as ESP32 and similar development boards that has PSRAM with Arduino platform. It is designed for resource-constrained environments but can be used with non-Arduino platform projects, offering a streamlined solution for tasks that require neural network capabilities.

Diwa stands out as a straightforward and effective solution for implementing artificial neural networks on microcontrollers. Key features include:

• Lightweight: Designed for resource-constrained microcontroller environments yet can still be used within non-Arduino environments.
• Simple Implementation: Provides a basic yet effective implementation of a Feedforward ANN.
• Easy Integration: Suitable for microcontrollers like ESP32 and similar boards with PSRAM.
• Training Support: Includes methods for training the neural network using backpropagation.

Note

Diwa is primarily intended for lightweight applications. For more intricate tasks, consider using advanced machine learning libraries on more powerful platforms.

See live demo on Wokwi.

Full Changelog: v0.0.1...v0.0.2

Diwa: Arduino Tiny AI/ML Library v0.0.1

Diwa: Arduino Tiny AI/ML Library

Diwa is a lightweight library providing a simple implementation of Feedforward Artificial Neural Networks (ANNs) for microcontrollers such as ESP8266, ESP32, and similar development boards. It is designed for resource-constrained environments, offering a streamlined solution for tasks that require neural network capabilities.

Diwa stands out as a straightforward and effective solution for implementing artificial neural networks on microcontrollers. Key features include:

• Lightweight: Designed for resource-constrained microcontroller environments.
• Simple Implementation: Provides a basic yet effective implementation of a Feedforward ANN.
• Easy Integration: Suitable for microcontrollers like ESP8266, ESP32, and similar boards.
• Training Support: Includes methods for training the neural network using backpropagation.

Note

Diwa is primarily intended for lightweight applications. For more intricate tasks, consider using advanced machine learning libraries on more powerful platforms.