85 citations · 285 across the 20 of their papers we have counts for
7 papers · 1 filter
PDP: Parameter-free Differentiable Pruning is All You Need
Minsik Cho, Saurabh Adya, Devang Naik
DNN pruning is a popular way to reduce the size of a model, improve the inference latency, and minimize the power consumption on DNN accelerators. However, existing approaches migh…
R2 Loss: Range Restriction Loss for Model Compression and Quantization
Arnav Kundu, Chungkuk Yoo, Srijan Mishra +2
Model quantization and compression is widely used techniques to reduce usage of computing resource at inference time. While state-of-the-art works have been achieved reasonable acc…
DKM: Differentiable K-Means Clustering Layer for Neural Network Compression
Minsik Cho, Keivan A. Vahid, Saurabh Adya +1
Deep neural network (DNN) model compression for efficient on-device inference is becoming increasingly important to reduce memory requirements and keep user data on-device. To this…
SimEx: Express Prediction of Inter-dataset Similarity by a Fleet of Autoencoders
Inseok Hwang, Jinho Lee, Frank Liu +1
Knowing the similarity between sets of data has a number of positive implications in training an effective model, such as assisting an informed selection out of known datasets favo…
MUTE: Data-Similarity Driven Multi-hot Target Encoding for Neural Network Design
Mayoore S. Jaiswal, Bumsoo Kang, Jinho Lee +1
Target encoding is an effective technique to deliver better performance for conventional machine learning methods, and recently, for deep neural networks as well. However, the exis…
A Unified Approximation Framework for Compressing and Accelerating Deep Neural Networks
Yuzhe Ma, Ran Chen, Wei Li +4
Deep neural networks (DNNs) have achieved significant success in a variety of real world applications, i.e., image classification. However, tons of parameters in the networks restr…
MEC: Memory-efficient Convolution for Deep Neural Network
Minsik Cho, Daniel Brand
Convolution is a critical component in modern deep neural networks, thus several algorithms for convolution have been developed. Direct convolution is simple but suffers from poor…