Narrow bit-width data formats are key to reducing the computational and storage costs of
modern deep learning applications. This paper evaluates Microscaling (MX) data formats …

Large-scale language models (LLMs) have demonstrated impressive performance, but their
deployment presents challenges due to their significant memory usage. This issue can be …

Data clipping is crucial in reducing noise in quantization operations and improving the
achievable accuracy of quantization-aware training (QAT). Current practices rely on …

This paper introduces Block Data Representations (BDR), a framework for exploring and
evaluating a wide spectrum of narrow-precision formats for deep learning. It enables …

In this work, for the first time, we demonstrate that computers can automatically learn from
observing the heuristic efforts of the last four decades, stand on the shoulders of the existing …

The energy efficiency of deep neural network (DNN) inference can be improved with custom
accelerators. DNN inference accelerators often employ specialized hardware techniques to …

Since the resurgence of deep neural networks (DNNs), image super-resolution (SR) has
recently seen a huge progress in improving the quality of low resolution images, however at …

Low-precision quantization is recognized for its efficacy in neural network optimization. Our
analysis reveals that non-quantized elementwise operations which are prevalent in layers …

Quantization has become a popular technique to compress neural networks and reduce
compute cost, but most prior work focuses on studying quantization without changing the …

Transformer based large language models have achieved tremendous success. However,
the significant memory and computational costs incurred during the inference process make …