6 papers
Fully Integrated Memristive Spiking Neural Network with Analog Neurons for High-Speed Event-Based Data Processing
Zhu Wang, Song Wang, Zhiyuan Du +5
The demand for edge artificial intelligence to process event-based, complex data calls for hardware beyond conventional digital, von-Neumann architectures. Neuromorphic computing,…
Trustworthy Tree-based Machine Learning by Flash-based Analog CAM with Inherent Soft Boundaries
Bo Wen, Guoyun Gao, Zhicheng Xu +5
The rapid advancement of artificial intelligence has raised concerns regarding its trustworthiness, especially in terms of interpretability and robustness. Tree-based models like R…
Fault-Free Analog Computing with Imperfect Hardware
Zhicheng Xu, Jiawei Liu, Sitao Huang +9
The growing demand for edge computing and AI drives research into analog in-memory computing using memristors, which overcome data movement bottlenecks by computing directly within…
Real-time raw signal genomic analysis using fully integrated memristor hardware
Peiyi He, Shengbo Wang, Ruibin Mao +6
Advances in third-generation sequencing have enabled portable and real-time genomic sequencing, but real-time data processing remains a bottleneck, hampering on-site genomic analys…
Efficient Nonlinear Function Approximation in Analog Resistive Crossbars for Recurrent Neural Networks
Junyi Yang, Ruibin Mao, Mingrui Jiang +9
Analog In-memory Computing (IMC) has demonstrated energy-efficient and low latency implementation of convolution and fully-connected layers in deep neural networks (DNN) by using p…
FeBiM: Efficient and Compact Bayesian Inference Engine Empowered with Ferroelectric In-Memory Computing
Chao Li, Zhicheng Xu, Bo Wen +5
In scenarios with limited training data or where explainability is crucial, conventional neural network-based machine learning models often face challenges. In contrast, Bayesian i…