Emerging multi-model workloads with heavy models like recent large language models
significantly increased the compute and memory demands on hardware. To address such …

With the ever increasing prevalence of neural networks and the upheaval from the language
models, it is time to rethink neural acceleration. Up to this point, the broader research …

A growing demand for hyperscale services has compelled hyperscalers to deploy more
compute resources at an unprecedented pace, further accelerated by the demise of …

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

There has been an arms race for devising accelerators for deep learning in recent years.
However, real-world applications are not only neural networks but often span across …

Quantization of foundational models (FMs) is significantly more challenging than traditional
DNNs due to the emergence of large magnitude features called outliers. Existing outlier …

Large Language Models achieve state of art performances on a broad variety of Natural
Language Processing tasks. In the pervasive IoT era, their deployment on edge devices is …

The remarkable performance of LLMs has led to their application in a wide range of fields,
with data centers utilizing expensive accelerators such as GPUs and TPUs to support LLM …

Meta’s Hyperscale Infrastructure: Overview and Insights Page 1 research DOI: 10.1145/3701296
BY CHUNQIANG TANG Meta’s Hyperscale Infrastructure: Overview and Insights A look at Meta's …

As computing systems become increasingly larger, high‐performance computing (HPC) is
gaining importance. In particular, as hyperscale artificial intelligence (AI) applications, such …