Electronic Design

Applying Edge AI to DC Arc Fault Detection (Part 2): Software Development to Deployment

Learn about the methodology and tools for AI-driven arc fault detection to create real-time classification on MCUs, improving ...

DEEPX and Ultralytics Forge Strategic Alliance to Define the Global Standard for Physical AI in the YOLO Community

DEEPX, a leading fabless AI semiconductor company specializing in ultra-low-power Neural Processing Units (NPUs), today ...
MUO on MSN

I was wrong about local LLMs, and these 4 myths were why

Stop thinking you need a $5,000 rig to run local AI — I finally ran a local AI on my old PC, and everything I believed was ...
How-To Geek on MSN

Don't pay for an AI coding assistant until you've tried running one locally

Your CPU can run a coding AI—here's why you shouldn't pay for one (as long as you have the patience for it).
GitHub

Quantization Experiments: Reproducing PolarQuant + QJL from Scratch

A research-grade implementation of low-bit quantization techniques inspired by Google Research's TurboQuant (ICLR 2026), built from scratch in Python with PyTorch. This repository documents a series ...
GitHub

compressed_tensors_moe.py

from sglang.srt.layers.moe.cutlass_moe_params import CutlassMoEParams, CutlassMoEType from sglang.srt.layers.moe.moe_runner.triton import TritonMoeQuantInfo from ...
IEEE

Data Quality-Aware Mixed-Precision Quantization via Hybrid Reinforcement Learning

Abstract: Mixed-precision quantization mostly predetermines the model bit-width settings before actual training due to the non-differential bit-width sampling process, obtaining suboptimal performance ...
IEEE

Improving Post-Training Quantization via Probabilistic Programming

Abstract: Post-training quantization (PTQ) is an effective solution for deploying deep neural networks on edge devices with limited resources. PTQ is especially attractive because it does not require ...