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

Quantization-Aware Training of Spiking Neural Networks for Energy-Efficient Spectrum Sensing on Loihi Chip

Abstract: Spectrum sensing is a technique used to identify idle/busy bandwidths in cognitive radio. Energy-efficient spectrum sensing is critical for multiple-input-multiple-output (MIMO) ...
Hosted on MSN

New guides show how to run massive AI models on modest PCs

Affordable AI hosting: New tutorials explain how to deploy large language models on low-cost hardware, reducing reliance on expensive GPUs and cloud subscriptions. Techniques that work: Layer ...
GitHub

KIVI: A Tuning-Free Asymmetric 2bit Quantization for KV Cache

[2025.09.25]: 🔥🔥🔥 We released a toolkit that tests the impact of numerical precision and enables deterministic LLM inference. This helps eliminate the training–inference mismatch in reinforcement ...