355 citations · 647 across the 23 of their papers we have counts for
24 papers
Architecture Carbon Tool v3: Enabling Sustainability-aware Silicon System Design Exploration
Vincent T. Lee, Bilge Acun, Zachary Lewis +1
As the carbon cost of manufacturing and operating semiconductor devices has come into sharper focus, sustainability has gradually emerged as a new system architecture design metric…
Agentic Discovery of Neural Architectures: AIRA-Compose and AIRA-Design
Alberto Pepe, Chien-Yu Lin, Despoina Magka +6
Toward recursive self-improvement, we investigate LLM agents autonomously designing foundation models beyond standard Transformers. We introduce a dual-framework approach: AIRA-Com…
Gecko: An Efficient Neural Architecture Inherently Processing Sequences with Arbitrary Lengths
Xuezhe Ma, Shicheng Wen, Linghao Jin +11
Designing a unified neural network to efficiently and inherently process sequential data with arbitrary lengths is a central and challenging problem in sequence modeling. The desig…
Hybrid Architectures for Language Models: Systematic Analysis and Design Insights
Sangmin Bae, Bilge Acun, Chien-Yu Lin +5
Recent progress in large language models demonstrates that hybrid architectures--combining self-attention mechanisms with structured state space models like Mamba--can achieve a co…
Composer: A Search Framework for Hybrid Neural Architecture Design
Bilge Acun, Prasoon Sinha, Newsha Ardalani +7
Hybrid model architectures that combine computational primitives (e.g., Attention, MLP) in different ratios have shown promising performance beyond Transformers. Some studies have…
Scalable LLM Reasoning Acceleration with Low-rank Distillation
Harry Dong, Bilge Acun, Beidi Chen +1
Due to long generations, large language model (LLM) math reasoning demands significant computational resources and time. While many existing efficient inference methods have been d…
CATransformers: Carbon Aware Transformers Through Joint Model-Hardware Optimization
Irene Wang, Newsha Ardalani, Mostafa Elhoushi +6
Machine learning solutions are rapidly adopted to enable a variety of key use cases, from conversational AI assistants to scientific discovery. This growing adoption is expected to…
Characterizing and Efficiently Accelerating Multimodal Generation Model Inference
Yejin Lee, Anna Sun, Basil Hosmer +27
Generative artificial intelligence (AI) technology is revolutionizing the computing industry. Not only its applications have broadened to various sectors but also poses new system…
Beyond Efficiency: Scaling AI Sustainably
Carole-Jean Wu, Bilge Acun, Ramya Raghavendra +1
Barroso's seminal contributions in energy-proportional warehouse-scale computing launched an era where modern datacenters have become more energy efficient and cost effective than…
Unlocking the Potential of Renewable Energy Through Curtailment Prediction
Bilge Acun, Brent Morgan, Henry Richardson +2
A significant fraction (5-15%) of renewable energy generated goes into waste in the grids around the world today due to oversupply issues and transmission constraints. Being able t…
Is Flash Attention Stable?
Alicia Golden, Samuel Hsia, Fei Sun +8
Training large-scale machine learning models poses distinct system challenges, given both the size and complexity of today's workloads. Recently, many organizations training state-…
CHAI: Clustered Head Attention for Efficient LLM Inference
Saurabh Agarwal, Bilge Acun, Basil Hosmer +5
Large Language Models (LLMs) with hundreds of billions of parameters have transformed the field of machine learning. However, serving these models at inference time is both compute…
LayerSkip: Enabling Early Exit Inference and Self-Speculative Decoding
Mostafa Elhoushi, Akshat Shrivastava, Diana Liskovich +10
We present LayerSkip, an end-to-end solution to speed-up inference of large language models (LLMs). First, during training we apply layer dropout, with low dropout rates for earlie…
Generative AI Beyond LLMs: System Implications of Multi-Modal Generation
Alicia Golden, Samuel Hsia, Fei Sun +8
As the development of large-scale Generative AI models evolve beyond text (1D) generation to include image (2D) and video (3D) generation, processing spatial and temporal informati…
Data Acquisition: A New Frontier in Data-centric AI
Lingjiao Chen, Bilge Acun, Newsha Ardalani +8
As Machine Learning (ML) systems continue to grow, the demand for relevant and comprehensive datasets becomes imperative. There is limited study on the challenges of data acquisiti…
Carbon Responder: Coordinating Demand Response for the Datacenter Fleet
Jiali Xing, Bilge Acun, Aditya Sundarrajan +5
The increasing integration of renewable energy sources results in fluctuations in carbon intensity throughout the day. To mitigate their carbon footprint, datacenters can implement…
MAD Max Beyond Single-Node: Enabling Large Machine Learning Model Acceleration on Distributed Systems
Samuel Hsia, Alicia Golden, Bilge Acun +5
Training and deploying large-scale machine learning models is time-consuming, requires significant distributed computing infrastructures, and incurs high operational costs. Our ana…
MP-Rec: Hardware-Software Co-Design to Enable Multi-Path Recommendation
Samuel Hsia, Udit Gupta, Bilge Acun +5
Deep learning recommendation systems serve personalized content under diverse tail-latency targets and input-query loads. In order to do so, state-of-the-art recommendation models…
DataPerf: Benchmarks for Data-Centric AI Development
Mark Mazumder, Colby Banbury, Xiaozhe Yao +42
Machine learning research has long focused on models rather than datasets, and prominent datasets are used for common ML tasks without regard to the breadth, difficulty, and faithf…
RecShard: Statistical Feature-Based Memory Optimization for Industry-Scale Neural Recommendation
Geet Sethi, Bilge Acun, Niket Agarwal +3
We propose RecShard, a fine-grained embedding table (EMB) partitioning and placement technique for deep learning recommendation models (DLRMs). RecShard is designed based on two ke…
Carbon Explorer: A Holistic Approach for Designing Carbon Aware Datacenters
Bilge Acun, Benjamin Lee, Fiodar Kazhamiaka +5
Technology companies have been leading the way to a renewable energy transformation, by investing in renewable energy sources to reduce the carbon footprint of their datacenters. I…
Sustainable AI: Environmental Implications, Challenges and Opportunities
Carole-Jean Wu, Ramya Raghavendra, Udit Gupta +22
This paper explores the environmental impact of the super-linear growth trends for AI from a holistic perspective, spanning Data, Algorithms, and System Hardware. We characterize t…
TT-Rec: Tensor Train Compression for Deep Learning Recommendation Models
Chunxing Yin, Bilge Acun, Xing Liu +1
The memory capacity of embedding tables in deep learning recommendation models (DLRMs) is increasing dramatically from tens of GBs to TBs across the industry. Given the fast growth…
Understanding Training Efficiency of Deep Learning Recommendation Models at Scale
Bilge Acun, Matthew Murphy, Xiaodong Wang +3
The use of GPUs has proliferated for machine learning workflows and is now considered mainstream for many deep learning models. Meanwhile, when training state-of-the-art personal r…