The past year has witnessed the increasing popularity of Large Language Models (LLMs).
Their unprecedented scale and associated high hardware cost have impeded their broader …

Large language models (LLMs) have shown remarkable performance across a wide range
of applications, often outperforming human experts. However, deploying these parameter …

Nowadays, artificial intelligence (AI) technology with large models plays an increasingly
important role in both academia and industry. It also brings a rapidly increasing demand for …

Many companies use large language models (LLMs) offered as a service, like OpenAl's GPT-
4, to create AI-enabled product experiences. Along with the benefits of ease-of-use and …

As large language models (LLMs) become widespread in various application domains, a
critical challenge the AI community is facing is how to train these large AI models in a cost …

Training and deploying large-scale machine learning models is time-consuming, requires
significant distributed computing infrastructures, and incurs high operational costs. Our …

The remarkable advancements in artificial intel-ligence (AI), primarily driven by deep neural
networks, are facing challenges surrounding unsustainable computational tra-jectories …

Over the past decade, machine learning model complexity has grown at an extraordinary
rate, as has the scale of the systems training such large models. However, there is an …

Aligning future system design with the ever-increasing compute needs of large language
models (LLMs) is undoubtedly an important problem in today's world. Here, we propose a …

Modern Artificial Intelligence (AI) workloads demand computing systems with large silicon
area to sustain throughput and competitive performance. However, prohibitive …