ONNX
ONNX on Beyond Market Intelligence: a running collection of 3 stories we have gathered and hand-picked because they are worth your time. Every post here touches on onnx 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 onnx, 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.
![I deployed a GAN on a Raspberry Pi 4 and built a physical NFT minting device [P]](https://preview.redd.it/k63kpgs2xu7h1.jpg?width=140&height=140&crop=1:1,smart&auto=webp&s=28094e042e5525ab21414c714edb14a51d731f8c)
I deployed a GAN on a Raspberry Pi 4 and built a physical NFT minting device [P]
Explore a unique convergence of AI and physical art: a generative adversarial network (GAN) deployed on a Raspberry Pi 4 to create a physical NFT minting device. This project, detailed by /u/Numerous-Dentist-882, trained a 128x128 DCGAN on Apple Silicon, achieving face hybrid generation in just 3 seconds per image. The device, built as an interactive art piece, allows users to generate and mint unique NFTs. For those interested in related research, see the recent discussion on "ICML (DL4C) Accepted" exploring deep learning at ICML.
INT8 quantization gives me better accuracy than FP16 ! [D]
In the world of deep learning, precision plays a crucial role in model performance. Recently, I observed an intriguing trend while comparing different precisions: FP32 served as my baseline, but surprisingly, INT8 post-training quantization yielded better inference accuracy than FP16. This outcome challenged my expectations, as I had assumed FP16 would be closer to FP32 in accuracy. I’m curious to hear if anyone else has experienced this phenomenon and what factors might explain why INT8 is outperforming FP16 in this context. Let's explore this together!
[P] Built a Interactive Web for PINN Solving the 2D Heat Equation
I’m excited to share my latest project: an interactive web app that utilizes Physics-Informed Neural Networks (PINNs) to solve the 2D heat equation for thermal simulations of circuit boards. This tool allows users to adjust parameters like chip power and ambient temperature, generating real-time heatmaps and hotspot temperatures. Built with a tech stack that includes Python, ONNX, and Blazor WebAssembly, I aim to make scientific AI accessible beyond research settings. I welcome your feedback and thoughts on the potential applications of this innovative approach.