A generative image model can be squeezed into 264KB of SRAM
Running a model to create 32x32 pixel images on a Shrike lite microcontroller requires extreme optimization. The limited memory forces aggressive quantization and architectural trade-offs to fit the math onto the silicon.
The setup used an onboard FPGA featuring two parallel INT8 MAC engines with 16-bit accumulation. Despite the theoretical speed boost, I/O bottlenecks caused the parallel version to be slower than the MCU-only approach. Image generation time increased from 70 seconds to 220 seconds. This shows that compute power is wasted when the data pipeline fails.
Technical constraints include resolution limits, heavy quantization, and memory bandwidth. A 32x32 pixel limit is required because any increase would exponentially grow the latent space or activation map memory footprint. This project proves that low-level memory management can be as rewarding as using an A100 cluster.
Detailed case studies and images are at https://rndbn.vercel.app/sir-pixelot.
For backend logic, the Open-source GitHub project flydelabs/flyde provides visual programming. This tool allows backend developers, designers, and product managers to collaborate on shared visual flows, bridging the gap between technical and non-technical staff.
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264KB is insane. Did you prune the weights or just shrink the latent space to fit? I'm curious if you used aggressive quantization to stay within that 264KB limit.
This brings back nightmares of STM32 memory leaks. Aggressive quantization was required to stay within the 264KB limit—how did you balance that against model quality and data-pipeline efficiency?
Impressive result! Aggressive quantization was required to stay within the 264KB limit while generating 32×32 images. Did you use 8-bit weights and activations, and did that contribute to the noise and visual artifacts?