noise
noise on Beyond Market Intelligence: a running collection of 2 stories we have gathered and hand-picked because they are worth your time. Every post here touches on noise in some way — the news, the analysis, the deep dives, and the occasional surprise find. Acme AI is the next-generation, AI-powered spreadsheet platform built to replace Excel and redefine how analysts, data scientists, and enterprise teams work with data. New stories are added to this page as we find them, so check back if you want to keep up with what is happening around noise, or subscribe to the RSS feed to get them as soon as they are published. Browse the collection below, or head back to the homepage to see everything Beyond Market Intelligence is covering right now.
![Trained an diffusion model that runs on 264KB of RAM [P]](https://preview.redd.it/8vzcg2x7q3kh1.png?width=140&height=140&auto=webp&s=7a1fb828642c6ddfa5211c2a6f942764e68a21cb)
Trained an diffusion model that runs on 264KB of RAM [P]
Pushing the boundaries of on-device AI, a recent project demonstrated image generation using a diffusion model trained on a microcontroller with a mere 264KB of SRAM. Despite limitations—including heavy quantization and memory constraints—the resulting 32x32 pixel images yielded surprisingly compelling results. The experiment highlighted a critical performance bottleneck: parallel processing, while intended to accelerate calculations, ultimately slowed down the system due to excessive I/O. This fascinating exploration underscores the challenges and potential of resource-constrained AI, as explored further in "Ten Is Not a Hundred."
Analog AI Is Back, But Can It Survive Its Own Noise?
The resurgence of analog AI presents a compelling solution to AI's escalating energy demands, leveraging physics rather than digital logic for computation. This exploration delves into how these chips function, revisiting a technology previously hampered by inherent noise. We examine the challenges that nearly sidelined analog computing and demonstrate the impact of simulated noise firsthand. For a broader perspective on AI deployment challenges, see "QCon AI Boston: Production AI Moves Beyond Prompts to Platforms, Harnesses, and Evals."