Recent advancements in machine learning achieved by Deep Neural Networks (DNNs)
have been significant. While demonstrating high accuracy, DNNs are associated with a …

Several post-training quantization methods have been applied to large language models
(LLMs), and have been shown to perform well down to 8-bits. We find that these methods …

Large language models (LLMs) have revolutionized natural language processing tasks.
However, their practical deployment is hindered by their immense memory and computation …

How can we compress language models without sacrificing accuracy? The number of
compression algorithms for language models is rapidly growing to benefit from remarkable …

Modern pre-trained transformers have rapidly advanced the state-of-the-art in machine
learning, but have also grown in parameters and computational complexity, making them …

Recent trends in deep learning (DL) imposed hardware accelerators as the most viable
solution for several classes of high-performance computing (HPC) applications such as …

The recent detection transformer (DETR) has advanced object detection, but its application
on resource-constrained devices requires massive computation and memory resources …

Weight oscillation is a by-product of quantization-aware training, in which quantized weights
frequently jump between two quantized levels, resulting in training instability and a sub …

While generative modeling has become prevalent across numerous research fields, its
integration into the realm of image retrieval remains largely unexplored and underjustified …

Memristors have emerged as promising devices for enabling efficient multiply-accumulate
(MAC) operations in crossbar arrays, crucial for analog in-memory computing (AiMC) …