456 citations · 868 across the 23 of their papers we have counts for
27 papers
Association profile conditioning in a set-temporal transformer for cross-session intracortical motor decoding
Xinyuan Zhang, Handong Mo, Pengfei Wen +5
Intracortical motor decoders degrade across sessions because the set of recorded units changes and persisting units can alter how their firing relates to behavior. Most existing me…
RRAM-DP: Device-Calibrated Differential Privacy for In-Memory Edge Learning
Kwunhang Wong, Jichang Yang, Karl M. H. Lai +7
Edge Artificial Intelligence of Things (AIoT) systems often collect sensitive data in situ, raising serious privacy concerns. Resistive-switching random-access memory (RRAM) is an…
Towards Secure and Efficient DNN Accelerators via Hardware-Software Co-Design
Wei Xuan, Zihao Xuan, Rongliang Fu +8
The rapid deployment of deep neural network (DNN) accelerators in safety-critical domains such as autonomous vehicles, healthcare systems, and financial infrastructure necessitates…
Parameter Efficient Machine Unlearning on Hybrid Resistive Memory based Compute-in-Memory Accelerators
Ning Lin, Jichang Yang, Yangu He +17
Resistive memory compute-in-memory accelerators provide energy efficient analogue matrix vector multiplication for neural network inference, but frequent reprogramming of analogue…
Efficient lattice field theory simulation using adaptive normalizing flow on a resistive memory-based neural differential equation solver
Meng Xu, Jichang Yang, Ning Lin +6
Lattice field theory (LFT) simulations underpin advances in classical statistical mechanics and quantum field theory, providing a unified computational framework across particle, n…
SeDA: Secure and Efficient DNN Accelerators with Hardware/Software Synergy
Wei Xuan, Zhongrui Wang, Lang Feng +6
Ensuring the confidentiality and integrity of DNN accelerators is paramount across various scenarios spanning autonomous driving, healthcare, and finance. However, current security…
Reconfigurable Digital RRAM Logic Enables In-Situ Pruning and Learning for Edge AI
Songqi Wang, Yue Zhang, Jia Chen +9
The human brain simultaneously optimizes synaptic weights and topology by growing, pruning, and strengthening synapses while performing all computation entirely in memory. In contr…
High-Level Surface Code Decoding via Parallel FFNNs on CIM Platforms
Hao Wang, Erjia Xiao, Wenbo Mu +9
Due to the high sensitivity of qubits to environmental noise, which leads to decoherence and information loss, active quantum error correction(QEC) is essential. Surface codes repr…
RNC: Efficient RRAM-aware NAS and Compilation for DNNs on Resource-Constrained Edge Devices
Kam Chi Loong, Shihao Han, Sishuo Liu +2
Computing-in-memory (CIM) is an emerging computing paradigm, offering noteworthy potential for accelerating neural networks with high parallelism, low latency, and energy efficienc…
Optical Spiking Neurons Enable High-Speed and Energy-Efficient Optical Neural Networks
Bo Xu, Zefeng Huang, Yuetong Fang +5
Optical neural networks (ONNs) perform extensive computations using photons instead of electrons, resulting in passively energy-efficient and low-latency computing. Among various O…
SNNGX: Securing Spiking Neural Networks with Genetic XOR Encryption on RRAM-based Neuromorphic Accelerator
Kwunhang Wong, Songqi Wang, Wei Huang +8
Biologically plausible Spiking Neural Networks (SNNs), characterized by spike sparsity, are growing tremendous attention over intellectual edge devices and critical bio-medical app…
RRAM-Based Bio-Inspired Circuits for Mobile Epileptic Correlation Extraction and Seizure Prediction
Hao Wang, Lingfeng Zhang, Erjia Xiao +3
Non-invasive mobile electroencephalography (EEG) acquisition systems have been utilized for long-term monitoring of seizures, yet they suffer from limited battery life. Resistive r…
Topology Optimization of Random Memristors for Input-Aware Dynamic SNN
Bo Wang, Shaocong Wang, Ning Lin +12
There is unprecedented development in machine learning, exemplified by recent large language models and world simulators, which are artificial neural networks running on digital co…
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…
Roadmap to Neuromorphic Computing with Emerging Technologies
Adnan Mehonic, Daniele Ielmini, Kaushik Roy +50
The roadmap is organized into several thematic sections, outlining current computing challenges, discussing the neuromorphic computing approach, analyzing mature and currently util…
Older and Wiser: The Marriage of Device Aging and Intellectual Property Protection of Deep Neural Networks
Ning Lin, Shaocong Wang, Yue Zhang +6
Deep neural networks (DNNs), such as the widely-used GPT-3 with billions of parameters, are often kept secret due to high training costs and privacy concerns surrounding the data u…
Continuous-Time Digital Twin with Analogue Memristive Neural Ordinary Differential Equation Solver
Hegan Chen, Jichang Yang, Jia Chen +20
Digital twins, the cornerstone of Industry 4.0, replicate real-world entities through computer models, revolutionising fields such as manufacturing management and industrial automa…
Efficient and accurate neural field reconstruction using resistive memory
Yifei Yu, Shaocong Wang, Woyu Zhang +16
Human beings construct perception of space by integrating sparse observations into massively interconnected synapses and neurons, offering a superior parallelism and efficiency. Re…
Resistive Memory-based Neural Differential Equation Solver for Score-based Diffusion Model
Jichang Yang, Hegan Chen, Jia Chen +19
Human brains image complicated scenes when reading a novel. Replicating this imagination is one of the ultimate goals of AI-Generated Content (AIGC). However, current AIGC methods,…
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…
Realizing In-Memory Baseband Processing for Ultra-Fast and Energy-Efficient 6G
Qunsong Zeng, Jiawei Liu, Mingrui Jiang +7
To support emerging applications ranging from holographic communications to extended reality, next-generation mobile wireless communication systems require ultra-fast and energy-ef…
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…
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…