DCGAN inference on a microcontroller: 12.6M parameters, 512KB SRAM, 26-second generation, pure C [P]

This post describes implementing DCGAN (Deep Convolutional Generative Adversarial Network) inference on resource-constrained microcontroller hardware, achieving image generation with a 12.6 million parameter model using only 512KB of SRAM and pure C code, with generation taking approximately 26 seconds per image. The work demonstrates the feasibility of running neural network inference on embedded systems with severe memory and computational limitations. This represents a significant technical achievement in deploying generative AI models to edge devices typically used in IoT and embedded applications.

Source: r/MachineLearning | 2026-05-25