Trained an diffusion model that runs on 264KB of RAM [P]
Mirrored from r/MachineLearning for archival readability. Support the source by reading on the original site.
| 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. [link] [comments] |
More from r/MachineLearning
-
How can I turn an industry ML project into a publication? [R]
Sep 28
-
Are there any good research papers around Text clustering using LLMs [R]
Sep 28
-
Free, open-source AI engineering course where you build each algorithm by hand: 523 lessons, now as EPUB/PDF books [P]
Sep 28
-
Two-stage shelf audit: YOLO finds the products, embeddings can't tell sibling SKUS apart. What should Stage 2 be? [P]
Sep 27
Discussion (0)
Sign in to join the discussion. Free account, 30 seconds — email code or GitHub.
Sign in →No comments yet. Sign in and be the first to say something.