40 citations · 51 across the 16 of their papers we have counts for
7 papers · 1 filter
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…
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…
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…
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…