BIOENGINEER

Teaching Tiny Networks: New Quantization Method Pushes 1-Bit AI Toward Full-Precision Accuracy

Researchers in South Korea have developed a progressive distillation framework called ASBQ that trains one-bit binary neural ...
Tech Times on MSN

LLM quantization without calibration data matches SmoothQuant: DualQuant targets FP4 hardware

LLM quantization without calibration data: DualQuant from AI inference startup d-Matrix eliminates the dataset requirement ...
InfoWorld

What is model quantization? Smaller, faster LLMs

Reducing the precision of model weights can make deep neural networks run faster in less GPU memory, while preserving model accuracy. If ever there were a salient example of a counter-intuitive ...
Nature

Quantization Techniques in Neural Network Inference

Quantization in neural network inference refers to the process of mapping high-precision parameters and activations to lower-precision representations, typically using integer or even binary values.
Semiconductor Engineering

Outlier-aware Quantization Framework Co-designed With Heterogeneous NVM For SLM Deployment on Edge Platforms (UCSD et al.)

A new technical paper titled “QMC: Efficient SLM Edge Inference via Outlier-Aware Quantization and Emergent Memories Co-Design” was published by researchers at University of California San Diego and ...