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08/30/2023
Latency-aware Unified Dynamic Networks for Efficient Image Recognition
Dynamic computation has emerged as a promising avenue to enhance the inf...
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04/19/2023
Post-Training Quantization for Object Detection
Efficient inference for object detection networks is a major challenge o...
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04/03/2023
RPTQ: Reorder-based Post-training Quantization for Large Language Models
Large-scale language models (LLMs) have demonstrated outstanding perform...
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03/23/2023
Benchmarking the Reliability of Post-training Quantization: a Particular Focus on Worst-case Performance
Post-training quantization (PTQ) is a popular method for compressing dee...
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12/14/2022
PD-Quant: Post-Training Quantization based on Prediction Difference Metric
As a neural network compression technique, post-training quantization (P...
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11/28/2022
Post-training Quantization on Diffusion Models
Denoising diffusion (score-based) generative models have recently achiev...
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10/12/2022
Latency-aware Spatial-wise Dynamic Networks
Spatial-wise dynamic convolution has become a promising approach to impr...
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11/24/2021
PTQ4ViT: Post-Training Quantization Framework for Vision Transformers
Quantization is one of the most effective methods to compress neural net...
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10/15/2021
PTQ-SL: Exploring the Sub-layerwise Post-training Quantization
Network quantization is a powerful technique to compress convolutional n...
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08/24/2021
METRO: A Software-Hardware Co-Design of Interconnections for Spatial DNN Accelerators
Tiled spatial architectures have proved to be an effective solution to b...
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09/19/2020
ENAS4D: Efficient Multi-stage CNN Architecture Search for Dynamic Inference
Dynamic inference is a feasible way to reduce the computational cost of ...
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11/16/2019
