anwaar-khalid

In Part 1, we looked at why rounding direction matters: a stochastic rounding experiment on a single ResNet-18 layer found assignments that beat round-to-nearest by over 10 points, just by choosing up vs. down more carefully.

Adaptive rounding techniques are one of the best ways to get more accuracy from quantization with the same bitwidth.

In my previous blogpost, we discussed in detail the outlier problem in transformers.

Modern Machine Learning models are trained with a large number of parameters, often too large, and this overparameterization is very useful during training as it creates a vast search space for the model to encode rich representations from data into its p…

A lot has happened in transformer quantization over the past few years, from barely being able to quantize a 7B model in INT8 without destroying accuracy, to routinely fitting a 70B model in 4-bits on a single GPU.

Machine Learning (ML) has witnessed a surge in interest in recent years driven by the availability of large-scale datasets, advances in ML frameworks such as PyTorch and TensorFlow, rise of hardware accelerators (e.g., GPUs and TPUs) that enable large-sca…

In this case study, we explore compressing neural networks for efficient deployment on edge devices with limited resources.