48 citations · 64 across the 4 of their papers we have counts for
5 papers
Low-power Spike-based Wearable Analytics on RRAM Crossbars
Abhiroop Bhattacharjee, Jinquan Shi, Wei-Chen Chen +2
This work introduces a spike-based wearable analytics system utilizing Spiking Neural Networks (SNNs) deployed on an In-memory Computing engine based on RRAM crossbars, which are k…
PowerGAN: A Machine Learning Approach for Power Side-Channel Attack on Compute-in-Memory Accelerators
Ziyu Wang, Yuting Wu, Yongmo Park +4
Analog compute-in-memory (CIM) systems are promising for deep neural network (DNN) inference acceleration due to their energy efficiency and high throughput. However, as the use of…
Bulk-Switching Memristor-based Compute-In-Memory Module for Deep Neural Network Training
Yuting Wu, Qiwen Wang, Ziyu Wang +5
The need for deep neural network (DNN) models with higher performance and better functionality leads to the proliferation of very large models. Model training, however, requires in…
RN-Net: Reservoir Nodes-Enabled Neuromorphic Vision Sensing Network
Sangmin Yoo, Eric Yeu-Jer Lee, Ziyu Wang +2
Event-based cameras are inspired by the sparse and asynchronous spike representation of the biological visual system. However, processing the event data requires either using expen…
Training Spiking Neural Networks Using Lessons From Deep Learning
Jason K. Eshraghian, Max Ward, Emre Neftci +6
The brain is the perfect place to look for inspiration to develop more efficient neural networks. The inner workings of our synapses and neurons provide a glimpse at what the futur…