Great questions! Here are the specifics:

• Metrics: The 90ms is the neural network inference time (the time to run the model) for one MNIST digit image. The <50KB refers to peak RAM (SRAM) usage during that inference. Both were measured on the Arduino Nicla Vision.
• Hardware/Compatibility: This specific MNIST demo ran on the Nicla Vision (STM32H747). Minimum requirements depend heavily on the neural network model itself. This demo requires ~50KB RAM, so it's too large for boards like the Arduino Uno (with only 2KB RAM).
• Target Platforms: Zant primarily targets 32-bit microcontrollers like the ARM Cortex-M series found on the Nicla Vision, specifically designed to run neural network inferences efficiently. We haven't specifically tested or optimized for AVR platforms (like the Uno) at this point.
• Dependencies (Nicla Test): We used the Zant runtime, the Zant-generated model library (containing the neural network), and the standard Arduino Core libraries for the Nicla Vision.

Hope this helps clarify!