13 citations · 23 across the 7 of their papers we have counts for
7 papers
Dynamic neural network with memristive CIM and CAM for 2D and 3D vision
Yue Zhang, Woyu Zhang, Shaocong Wang +14
The brain is dynamic, associative and efficient. It reconfigures by associating the inputs with past experiences, with fused memory and processing. In contrast, AI models are stati…
Random resistive memory-based deep extreme point learning machine for unified visual processing
Shaocong Wang, Yizhao Gao, Yi Li +21
Visual sensors, including 3D LiDAR, neuromorphic DVS sensors, and conventional frame cameras, are increasingly integrated into edge-side intelligent machines. Realizing intensive m…
Pruning random resistive memory for optimizing analogue AI
Yi Li, Songqi Wang, Yaping Zhao +17
The rapid advancement of artificial intelligence (AI) has been marked by the large language models exhibiting human-like intelligence. However, these models also present unpreceden…
Resistive memory-based zero-shot liquid state machine for multimodal event data learning
Ning Lin, Shaocong Wang, Yi Li +19
The human brain is a complex spiking neural network (SNN), capable of learning multimodal signals in a zero-shot manner by generalizing existing knowledge. Remarkably, it maintains…
Binary stochasticity enabled highly efficient neuromorphic deep learning achieves better-than-software accuracy
Yang Li, Wei Wang, Ming Wang +13
Deep learning needs high-precision handling of forwarding signals, backpropagating errors, and updating weights. This is inherently required by the learning algorithm since the gra…
Efficient Training of the Memristive Deep Belief Net Immune to Non-Idealities of the Synaptic Devices
Wei Wang, Barak Hoffer, Tzofnat Greenberg-Toledo +8
The tunability of conductance states of various emerging non-volatile memristive devices emulates the plasticity of biological synapses, making it promising in the hardware realiza…
Echo state graph neural networks with analogue random resistor arrays
Shaocong Wang, Yi Li, Dingchen Wang +13
Recent years have witnessed an unprecedented surge of interest, from social networks to drug discovery, in learning representations of graph-structured data. However, graph neural…