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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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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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12/07/2021
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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02/20/2021
An Evaluation of Edge TPU Accelerators for Convolutional Neural Networks
Edge TPUs are a domain of accelerators for low-power, edge devices and a...
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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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04/22/2020
Chip Placement with Deep Reinforcement Learning
In this work, we present a learning-based approach to chip placement, on...
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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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04/16/2017