3.7k citations · 5.6k across the 34 of their papers we have counts for
15 papers · 1 filter
IC-Cache: Efficient Large Language Model Serving via In-context Caching
Yifan Yu, Yu Gan, Nikhil Sarda +7
Large language models (LLMs) have excelled in various applications, yet serving them at scale is challenging due to their substantial resource demands and high latency. Our real-wo…
FLuRKA: Fast and accurate unified Low-Rank & Kernel Attention
Ahan Gupta, Hao Guo, Yueming Yuan +2
Many efficient self-attention techniques have become prevalent since the inception of the transformer architecture. Two popular classes of these techniques a…
Brainformers: Trading Simplicity for Efficiency
Yanqi Zhou, Nan Du, Yanping Huang +12
Transformers are central to recent successes in natural language processing and computer vision. Transformers have a mostly uniform backbone where layers alternate between feed-for…
GiPH: Generalizable Placement Learning for Adaptive Heterogeneous Computing
Yi Hu, Chaoran Zhang, Edward Andert +6
Careful placement of a computational application within a target device cluster is critical for achieving low application completion time. The problem is challenging due to its NP-…
Learning Large Graph Property Prediction via Graph Segment Training
Kaidi Cao, Phitchaya Mangpo Phothilimthana, Sami Abu-El-Haija +5
Learning to predict properties of large graphs is challenging because each prediction requires the knowledge of an entire graph, while the amount of memory available during trainin…
Mixture-of-Experts with Expert Choice Routing
Yanqi Zhou, Tao Lei, Hanxiao Liu +7
Sparsely-activated Mixture-of-experts (MoE) models allow the number of parameters to greatly increase while keeping the amount of computation for a given token or a given sample un…
A Transferable Approach for Partitioning Machine Learning Models on Multi-Chip-Modules
Xinfeng Xie, Prakash Prabhu, Ulysse Beaugnon +6
Multi-Chip-Modules (MCMs) reduce the design and fabrication cost of machine learning (ML) accelerators while delivering performance and energy efficiency on par with a monolithic l…
Rethinking Co-design of Neural Architectures and Hardware Accelerators
Yanqi Zhou, Xuanyi Dong, Berkin Akin +7
Neural architectures and hardware accelerators have been two driving forces for the progress in deep learning. Previous works typically attempt to optimize hardware given a fixed m…
Apollo: Transferable Architecture Exploration
Amir Yazdanbakhsh, Christof Angermueller, Berkin Akin +7
The looming end of Moore's Law and ascending use of deep learning drives the design of custom accelerators that are optimized for specific neural architectures. Architecture explor…
Do Transformer Modifications Transfer Across Implementations and Applications?
Sharan Narang, Hyung Won Chung, Yi Tay +13
The research community has proposed copious modifications to the Transformer architecture since it was introduced over three years ago, relatively few of which have seen widespread…
Transferable Graph Optimizers for ML Compilers
Yanqi Zhou, Sudip Roy, Amirali Abdolrashidi +9
Most compilers for machine learning (ML) frameworks need to solve many correlated optimization problems to generate efficient machine code. Current ML compilers rely on heuristics…
Exploring the Limits of Transfer Learning with a Unified Text-to-Text Transformer
Colin Raffel, Noam Shazeer, Adam Roberts +6
Transfer learning, where a model is first pre-trained on a data-rich task before being fine-tuned on a downstream task, has emerged as a powerful technique in natural language proc…
GDP: Generalized Device Placement for Dataflow Graphs
Yanqi Zhou, Sudip Roy, Amirali Abdolrashidi +8
Runtime and scalability of large neural networks can be significantly affected by the placement of operations in their dataflow graphs on suitable devices. With increasingly comple…
EPNAS: Efficient Progressive Neural Architecture Search
Yanqi Zhou, Peng Wang, Sercan Arik +4
In this paper, we propose Efficient Progressive Neural Architecture Search (EPNAS), a neural architecture search (NAS) that efficiently handles large search space through a novel p…
Deep Learning Scaling is Predictable, Empirically
Joel Hestness, Sharan Narang, Newsha Ardalani +6
Deep learning (DL) creates impactful advances following a virtuous recipe: model architecture search, creating large training data sets, and scaling computation. It is widely belie…