Trained an diffusion model that runs on 264KB of RAM [P]

Reddit r/MachineLearning 08/18/26, 09:26 AM News

diffusion-model microcontroller fpga edge-ai memory-optimization image-generation

Summary

An individual trained a diffusion model to generate 32x32 pixel images on a Shrike lite microcontroller with only 264KB RAM, experimenting with FPGA acceleration that hit memory bottlenecks, resulting in noisy but sometimes interesting outputs.

I recently bought a Shrike lite which has got 264KB of SRAM. I decided to train an image generation model that generates 32*32 pixel images. The microcontroller also has an FPGA onboard which I used to create two parallel INT8 MAC engines with 16 bit accumulation to speed up calculations, however the system soon hit a memory wall due to the high number of I/O operations, this meant that the system with parallel MAC engines ran slower than the MCU only model (~220 seconds per image vs ~70 seconds per image). It was still a fun project that I enjoyed messing around with. A lot of the images looked weird and noisy because of the heavy quantization and memory limits but some of them came out cool. Full case study here.

Original Article

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