16 citations · 31 across the 11 of their papers we have counts for
11 papers
Learnable Mixed-precision and Dimension Reduction Co-design for Low-storage Activation
Yu-Shan Tai, Cheng-Yang Chang, Chieh-Fang Teng +2
Recently, deep convolutional neural networks (CNNs) have achieved many eye-catching results. However, deploying CNNs on resource-constrained edge devices is constrained by limited…
Compression-aware Projection with Greedy Dimension Reduction for Convolutional Neural Network Activations
Yu-Shan Tai, Chieh-Fang Teng, Cheng-Yang Chang +1
Convolutional neural networks (CNNs) achieve remarkable performance in a wide range of fields. However, intensive memory access of activations introduces considerable energy consum…
Neural Network-Aided BCJR Algorithm for Joint Symbol Detection and Channel Decoding
Wen-Chiao Tsai, Chieh-Fang Teng, Han-Mo Ou +1
Recently, deep learning-assisted communication systems have achieved many eye-catching results and attracted more and more researchers in this emerging field. Instead of completely…
Accumulated Polar Feature-based Deep Learning for Efficient and Lightweight Automatic Modulation Classification with Channel Compensation Mechanism
Chieh-Fang Teng, Ching-Yao Chou, Chun-Hsiang Chen +1
In next-generation communications, massive machine-type communications (mMTC) induce severe burden on base stations. To address such an issue, automatic modulation classification (…
Syndrome-Enabled Unsupervised Learning for Neural Network-Based Polar Decoder and Jointly Optimized Blind Equalizer
Chieh-Fang Teng, Yen-Liang Chen
Recently, the syndrome loss has been proposed to achieve "unsupervised learning" for neural network-based BCH/LDPC decoders. However, the design approach cannot be applied to polar…
Low-Complexity LSTM-Assisted Bit-Flipping Algorithm for Successive Cancellation List Polar Decoder
Chun-Hsiang Chen, Chieh-Fang Teng, An-Yeu Wu
Polar codes have attracted much attention in the past decade due to their capacity-achieving performance. The higher decoding capacity is required for 5G and beyond 5G (B5G). Altho…
Unsupervised Learning for Neural Network-based Polar Decoder via Syndrome Loss
Chieh-Fang Teng, An-Yeu Wu
With the rapid growth of deep learning in many fields, machine learning-assisted communication systems had attracted lots of researches with many eye-catching initial results. At t…
Convolutional Neural Network-aided Bit-flipping for Belief Propagation Decoding of Polar Codes
Chieh-Fang Teng, Kuan-Shiuan Ho, Chen-Hsi Wu +2
Known for their capacity-achieving abilities, polar codes have been selected as the control channel coding scheme for 5G communications. To satisfy the needs of high throughput and…
Neural Network-based Equalizer by Utilizing Coding Gain in Advance
Chieh-Fang Teng, Han-Mo Ou, An-Yeu Wu
Recently, deep learning has been exploited in many fields with revolutionary breakthroughs. In the light of this, deep learning-assisted communication systems have also attracted m…
Polar Feature Based Deep Architectures for Automatic Modulation Classification Considering Channel Fading
Chieh-Fang Teng, Ching-Chun Liao, Chun-Hsiang Chen +1
To develop intelligent receivers, automatic modulation classification (AMC) plays an important role for better spectrum utilization. The emerging deep learning (DL) technique has r…
Low-complexity Recurrent Neural Network-based Polar Decoder with Weight Quantization Mechanism
Chieh-Fang Teng, Chen-Hsi Wu, Kuan-Shiuan Ho +1
Polar codes have drawn much attention and been adopted in 5G New Radio (NR) due to their capacity-achieving performance. Recently, as the emerging deep learning (DL) technique has…