About The Workshop
Training and deployment of huge machine learning models, such as GPT, Llama, or large GNNs, require a vast amount of compute resources, power, storage, memory. The size of such models is growing exponentially, as is the training time and the resources required. The cost to train large foundation models has become prohibitive for everyone but very few large players. While the challenges are most visible in training, similar considerations apply to deploying and serving large foundation models for a large user base.
The proposed workshop aims to bring together AI/ML researchers, computer architects, and engineers working on a range of topics focused on training and serving large ML models. The workshop will provide a forum for presenting and exchanging new ideas and experiences in this area and to discuss and explore hardware/software techniques and tools to lower the significant barrier of entry in the computation requirements of AI foundation models.
Motivation
We are seeking innovative, evolutionary and revolutionary ideas around software and hardware architectures for training such challenging models and strive to present and discuss new approaches that may lead to alternative solutions.
Location
The workshop will be held in Buenos Aires, Argentina.
The workshop will be co-located with ISCA 2024.
Date: 30 June 2024
Invited Talks
Igor Arsovski
Chief architect at Groq
Think Faster: 1300 t/s/u on Llama3 with Groq software scheduled LPU Inference Engine
Sumanth Gudaparthi
Member of Technical Staff, AMD Research
Training Massive-Scale AI Foundational Models on Frontier
Event Schedule
ARC-LG workshop on Large Language Models and Graph Neural Networks
Welcome
Registrations and Welcome note
Session 1 Chair: David Kaeli
Can Tree-Based Model Improve Performance Prediction for LLMs?
Karthick Panner Selvam and Mats Brorsson
[slides]CaR: An Efficient KV Cache Reuse System for Large Language Model Inference
Kexin Chu, Tzechinh Liu, Yunding Li, Pengchao Yuan and Wei Zhang
[slides]LGNNIC: Acceleration of Large-Scale GNN Training using SmartNICs
Liad Gerstman, Aditya Dhakal, Sai Rahul Chalamalasetti, Chaim Baskin and Dejan Milojicic
[slides]
Invited Talk Igor Arsovski GROQ
Think Faster:1300 t/s/u on Llama3 with Groq software scheduled LPU Inference Engine
[slides]Lunch
Break for Lunch
Session 2 Chair: Avi Mendelson
Comparing Data Precision on Low-Rank Adaptation for Fine-tuning Large Language Models
Bagus Hanindhito, Bhavesh Patel and Lizy K. John.
[slides]Casting off the Old Guard: Achieving Superior A.I. Performance through Simplification
Jerry Felix, Steve Brunker and Carol Hibbard
[slides]SECDA-LLM: Designing Efficient LLM Accelerators for Edge Devices
Jude Haris, Rappy Saha, Wenhao Hu and José Cano
[slides]
Invited Talk Sumanth Gudaparthi AMD
Training Massive-Scale AI Foundational Models on Frontier
[slides]Break
Break for Snacks
Session 3 Chair: Paolo Faraboschi
PrefixSmart: Enhancing Large Language Model Efficiency through Advanced Prompt Management
Yunding Li, Kexin Chu, Nannan Zhao and Wei Zhang
[slides]hLLM: A Numa-aware Heterogeneous Platform for High-throughput Large Language Models Service
Kexin Chu, Tzechinh Liu, Pengchao Yuan and Wei Zhang
[slides]LLM-VeriPPA: Power, Performance, and Area-aware Verilog Code Generation and Refinement with Large Language Models
Kiran Gautam Thorat, Amit Hasan, Jiahui Zhao, Yaotian Liu, Xi Xie, Hongwu Peng, Bin Lei, Jeff Zhang and Caiwen Ding
[slides]PANEL: What is the path forward to environmentally friendly LLMs?
Lizy John, Univ. of Texas
Josep Torrellas, UIUC
Dr. Binbin Meng, Huawei
Closing
Concluding remarks
ORGANIZATION
Program Co-Chairs
Avi Mendelson Technion
David Kaeli Northeastern University
Paolo Faraboschi Hewlett Packard Labs
Program Committee
Jose Luis Abellan University of Murcia
Rosa M Badia Barcelona Supercomputer Center
Chaim Baskin Technion
Jose Cano University of Glasgow
Freddy Gabbay Ruppin College
John Kim KAIST
Dejan S. Milojicic HPE
Alexandra Posoldova Sigma
Bin Ren William and Mary
Carole Jean Wu META
Jhibin Yu Shenzhen Institute of Technology
Kaustubh Shivdikar Northeastern University
Publicity Chair
Pavana Prakash Hewlett Packard Labs
Web Chair
Kaustubh Shivdikar Northeastern University
Contact Us
For queries regarding submission