21 citations · 29 across the 6 of their papers we have counts for
6 papers
SlimPer: Make Personalization Model Slim and Smart
Siqi Wang, Xianjie Chen, Shaofeng Deng +42
Transformer-style architectures are increasingly adopted for industrial recommendation systems, yet they inherit a design premise misaligned with the task: generative models rely o…
NNStreamer: Efficient and Agile Development of On-Device AI Systems
MyungJoo Ham, Jijoong Moon, Geunsik Lim +9
We propose NNStreamer, a software system that handles neural networks as filters of stream pipelines, applying the stream processing paradigm to deep neural network applications. A…
Network Pruning for Low-Rank Binary Indexing
Dongsoo Lee, Se Jung Kwon, Byeongwook Kim +2
Pruning is an efficient model compression technique to remove redundancy in the connectivity of deep neural networks (DNNs). Computations using sparse matrices obtained by pruning…
Structured Compression by Weight Encryption for Unstructured Pruning and Quantization
Se Jung Kwon, Dongsoo Lee, Byeongwook Kim +3
Model compression techniques, such as pruning and quantization, are becoming increasingly important to reduce the memory footprints and the amount of computations. Despite model si…
DeepTwist: Learning Model Compression via Occasional Weight Distortion
Dongsoo Lee, Parichay Kapoor, Byeongwook Kim
Model compression has been introduced to reduce the required hardware resources while maintaining the model accuracy. Lots of techniques for model compression, such as pruning, qua…
A method of limiting performance loss of CNNs in noisy environments
James R. Geraci, Parichay Kapoor
Convolutional Neural Network (CNN) recognition rates drop in the presence of noise. We demonstrate a novel method of counteracting this drop in recognition rate by adjusting the bi…