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06/30/2022
QUIDAM: A Framework for Quantization-Aware DNN Accelerator and Model Co-Exploration
As the machine learning and systems communities strive to achieve higher...
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05/20/2022
QADAM: Quantization-Aware DNN Accelerator Modeling for Pareto-Optimality
As the machine learning and systems communities strive to achieve higher...
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05/17/2022
QAPPA: Quantization-Aware Power, Performance, and Area Modeling of DNN Accelerators
As the machine learning and systems community strives to achieve higher ...
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07/01/2019
Single-Path Mobile AutoML: Efficient ConvNet Design and NAS Hyperparameter Optimization
Can we reduce the search cost of Neural Architecture Search (NAS) from d...
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06/19/2019
ViP: Virtual Pooling for Accelerating CNN-based Image Classification and Object Detection
In recent years, Convolutional Neural Networks (CNNs) have shown superio...
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05/10/2019
Single-Path NAS: Device-Aware Efficient ConvNet Design
Can we automatically design a Convolutional Network (ConvNet) with the h...
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04/28/2019
LeGR: Filter Pruning via Learned Global Ranking
Filter pruning has shown to be effective for learning resource-constrain...
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04/05/2019
Single-Path NAS: Designing Hardware-Efficient ConvNets in less than 4 Hours
Can we automatically design a Convolutional Network (ConvNet) with the h...
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04/05/2019
FLightNNs: Lightweight Quantized Deep Neural Networks for Fast and Accurate Inference
To improve the throughput and energy efficiency of Deep Neural Networks ...
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04/04/2019
Regularizing Activation Distribution for Training Binarized Deep Networks
Binarized Neural Networks (BNNs) can significantly reduce the inference ...
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02/08/2019
AdaScale: Towards Real-time Video Object Detection Using Adaptive Scaling
In vision-enabled autonomous systems such as robots and autonomous cars,...
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01/21/2019
Understanding the Impact of Label Granularity on CNN-based Image Classification
In recent years, supervised learning using Convolutional Neural Networks...
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12/02/2017