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08/16/2023
BREATHE: Second-Order Gradients and Heteroscedastic Emulation based Design Space Exploration
Researchers constantly strive to explore larger and more complex search ...
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03/27/2023
TransCODE: Co-design of Transformers and Accelerators for Efficient Training and Inference
Automated co-design of machine learning models and evaluation hardware i...
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03/24/2023
EdgeTran: Co-designing Transformers for Efficient Inference on Mobile Edge Platforms
Automated design of efficient transformer models has recently attracted ...
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02/28/2023
AccelTran: A Sparsity-Aware Accelerator for Dynamic Inference with Transformers
Self-attention-based transformer models have achieved tremendous success...
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12/07/2022
CODEBench: A Neural Architecture and Hardware Accelerator Co-Design Framework
Recently, automated co-design of machine learning (ML) models and accele...
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05/23/2022
FlexiBERT: Are Current Transformer Architectures too Homogeneous and Rigid?
The existence of a plethora of language models makes the problem of sele...
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10/06/2021
Generative Optimization Networks for Memory Efficient Data Generation
In standard generative deep learning models, such as autoencoders or GAN...
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05/15/2021
Are Convolutional Neural Networks or Transformers more like human vision?
Modern machine learning models for computer vision exceed humans in accu...
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05/06/2020
AVAC: A Machine Learning based Adaptive RRAM Variability-Aware Controller for Edge Devices
Recently, the Edge Computing paradigm has gained significant popularity ...
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10/21/2019
Transformative effects of IoT, Blockchain and Artificial Intelligence on cloud computing: Evolution, vision, trends and open challenges
Cloud computing plays a critical role in modern society and enables a ra...
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10/03/2019
APEX: Adaptive Ext4 File System for Enhanced Data Recoverability in Edge Devices
Recently Edge Computing paradigm has gained significant popularity both ...
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11/29/2018
