Generative Large Language Models (LLMs) have demonstrated remarkable results for a
wide range of tasks. However, deploying these models for inference has been a significant …

Recent breakthroughs in brain-inspired computing promise to address a wide range of
problems from security to healthcare. However, the current strategy of implementing artificial …

Pretrained large language models (LLMs) exhibit exceptional general language processing
capabilities but come with significant demands on memory and computational resources. As …

In computer architecture studies, simulators are crucial for design verification, reducing
research and development time and ensuring the high accuracy of verification results …

The deployment of large language models (LLMs) is often constrained by memory
bandwidth, where the primary bottleneck is the cost of transferring model parameters from …

The rapid progress in Deep Learning (DL) and Large Language Models (LLMs) has
exponentially increased demands of computational power and bandwidth. This, combined …

In recent years, we are witnessing a trend toward in-memory computing for future
generations of computers that differs from traditional von-Neumann architecture in which …

Large matrices arise in many machine learning and data analysis applications, including as
representations of datasets, graphs, model weights, and first and second-order derivatives …

Bit-level sparsity methods skip ineffectual zero-bit operations and are typically applicable
within bit-serial deep learning accelerators. This type of sparsity at the bit-level is especially …

Large language models (LLMs) have shown impressive performance on language tasks but
face challenges when deployed on resource-constrained devices due to their extensive …