LLMCompass: Enabling Efficient Hardware Design for Large Language Model Inference
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 …
Their unprecedented scale and associated high hardware cost have impeded their broader …
Chakra: Advancing performance benchmarking and co-design using standardized execution traces
Benchmarking and co-design are essential for driving optimizations and innovation around
ML models, ML software, and next-generation hardware. Full workload benchmarks, eg …
ML models, ML software, and next-generation hardware. Full workload benchmarks, eg …
Wafer-Scale Computing: Advancements, Challenges, and Future Perspectives [Feature]
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 …
important role in both academia and industry. It also brings a rapidly increasing demand for …
LIBRA: Enabling Workload-Aware Multi-Dimensional Network Topology Optimization for Distributed Training of Large AI Models
As model sizes in machine learning continue to scale, distributed training is necessary to
accommodate model weights within each device and to reduce training time. However, this …
accommodate model weights within each device and to reduce training time. However, this …
DeepFlow: A cross-stack pathfinding framework for distributed ai systems
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 …
rate, as has the scale of the systems training such large models. However, there is an …
MAD-Max Beyond Single-Node: Enabling Large Machine Learning Model Acceleration on Distributed Systems
Training and deploying large-scale machine learning models is time-consuming, requires
significant distributed computing infrastructures, and incurs high operational costs. Our …
significant distributed computing infrastructures, and incurs high operational costs. Our …
Scaling Down to Scale Up: A Cost-Benefit Analysis of Replacing OpenAI's GPT-4 with Self-Hosted Open Source SLMs in Production
Many companies rely on APIs of managed AI models such as OpenAI's GPT-4 to create AI-
enabled experiences in their products. Along with the benefits of ease of use and shortened …
enabled experiences in their products. Along with the benefits of ease of use and shortened …
The Case For Data Centre Hyperloops
Data movement is a hot-button topic today, with workloads like machine learning (ML)
training, graph processing, and data analytics consuming datasets as large as 30PB. Such a …
training, graph processing, and data analytics consuming datasets as large as 30PB. Such a …
TACOS: Topology-Aware Collective Algorithm Synthesizer for Distributed Machine Learning
W Won, M Elavazhagan, S Srinivasan, A Durg… - arXiv preprint arXiv …, 2023 - arxiv.org
The surge of artificial intelligence, specifically large language models, has led to a rapid
advent towards the development of large-scale machine learning training clusters …
advent towards the development of large-scale machine learning training clusters …
Demystifying Platform Requirements for Diverse LLM Inference Use Cases
Large language models (LLMs) have shown remarkable performance across a wide range
of applications, often outperforming human experts. However, deploying these parameter …
of applications, often outperforming human experts. However, deploying these parameter …