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06/27/2023
FLuRKA: Fast fused Low-Rank Kernel Attention
Many efficient approximate self-attention techniques have become prevale...
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05/29/2023
Brainformers: Trading Simplicity for Efficiency
Transformers are central to recent successes in natural language process...
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05/24/2023
Flan-MoE: Scaling Instruction-Finetuned Language Models with Sparse Mixture of Experts
The explosive growth of language models and their applications have led ...
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05/23/2023
GiPH: Generalizable Placement Learning for Adaptive Heterogeneous Computing
Careful placement of a computational application within a target device ...
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05/20/2023
Lifelong Language Pretraining with Distribution-Specialized Experts
Pretraining on a large-scale corpus has become a standard method to buil...
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04/11/2023
Conditional Adapters: Parameter-efficient Transfer Learning with Fast Inference
We propose Conditional Adapter (CoDA), a parameter-efficient transfer le...
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10/04/2022
Toward Edge-Efficient Dense Predictions with Synergistic Multi-Task Neural Architecture Search
In this work, we propose a novel and scalable solution to address the ch...
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04/09/2022
Searching for Efficient Neural Architectures for On-Device ML on Edge TPUs
On-device ML accelerators are becoming a standard in modern mobile syste...
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02/18/2022
Mixture-of-Experts with Expert Choice Routing
Sparsely-activated Mixture-of-experts (MoE) models allow the number of p...
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12/13/2021
GLaM: Efficient Scaling of Language Models with Mixture-of-Experts
Scaling language models with more data, compute and parameters has drive...
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12/07/2021
A Transferable Approach for Partitioning Machine Learning Models on Multi-Chip-Modules
Multi-Chip-Modules (MCMs) reduce the design and fabrication cost of mach...
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02/23/2021
Do Transformer Modifications Transfer Across Implementations and Applications?
The research community has proposed copious modifications to the Transfo...
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02/17/2021
Rethinking Co-design of Neural Architectures and Hardware Accelerators
Neural architectures and hardware accelerators have been two driving for...
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02/02/2021
Apollo: Transferable Architecture Exploration
The looming end of Moore's Law and ascending use of deep learning drives...
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10/21/2020
Transferable Graph Optimizers for ML Compilers
Most compilers for machine learning (ML) frameworks need to solve many c...
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08/03/2020
A Learned Performance Model for Tensor Processing Units
Accurate hardware performance models are critical to efficient code gene...
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07/03/2020
ODE-CNN: Omnidirectional Depth Extension Networks
Omnidirectional 360 camera proliferates rapidly for autonomous robots si...
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10/23/2019
Exploring the Limits of Transfer Learning with a Unified Text-to-Text Transformer
Transfer learning, where a model is first pre-trained on a data-rich tas...
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09/28/2019
GDP: Generalized Device Placement for Dataflow Graphs
Runtime and scalability of large neural networks can be significantly af...
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07/07/2019
EPNAS: Efficient Progressive Neural Architecture Search
In this paper, we propose Efficient Progressive Neural Architecture Sear...
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06/12/2018
Resource-Efficient Neural Architect
Neural Architecture Search (NAS) is a laborious process. Prior work on a...
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02/14/2018
Neural Voice Cloning with a Few Samples
Voice cloning is a highly desired feature for personalized speech interf...
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12/01/2017
Deep Learning Scaling is Predictable, Empirically
Deep learning (DL) creates impactful advances following a virtuous recip...
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05/24/2017