5 papers
Neural-HAR: A Dimension-Gated CNN Accelerator for Real-Time Radar Human Activity Recognition
Yizhuo Wu, Francesco Fioranelli, Chang Gao
Radar-based human activity recognition (HAR) is attractive for unobtrusive and privacy-preserving monitoring, yet many CNN/RNN solutions remain too heavy for edge deployment, and e…
SparseDPD: A Sparse Neural Network-based Digital Predistortion FPGA Accelerator for RF Power Amplifier Linearization
Manno Versluis, Yizhuo Wu, Chang Gao
Digital predistortion (DPD) is crucial for linearizing radio frequency (RF) power amplifiers (PAs), improving signal integrity and efficiency in wireless systems. Neural network (N…
DeltaDPD: Exploiting Dynamic Temporal Sparsity in Recurrent Neural Networks for Energy-Efficient Wideband Digital Predistortion
Yizhuo Wu, Yi Zhu, Kun Qian +5
Digital Predistortion (DPD) is a popular technique to enhance signal quality in wideband RF power amplifiers (PAs). With increasing bandwidth and data rates, DPD faces significant…
RadMamba: Efficient Human Activity Recognition through Radar-based Micro-Doppler-Oriented Mamba State-Space Model
Yizhuo Wu, Francesco Fioranelli, Chang Gao
Radar-based Human Activity Recognition (HAR) is an attractive alternative to wearables and cameras because it preserves privacy, and is contactless and robust to occlusions. Howeve…
DPD-NeuralEngine: A 22-nm 6.6-TOPS/W/mm Recurrent Neural Network Accelerator for Wideband Power Amplifier Digital Pre-Distortion
Ang Li, Haolin Wu, Yizhuo Wu +3
The increasing adoption of Deep Neural Network (DNN)-based Digital Pre-distortion (DPD) in modern communication systems necessitates efficient hardware implementations. This paper…