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20242026
most citedTopology Optimization of Random Memristors for Input-Aware Dynamic SNN

1 citations · 1 across the 6 of their papers we have counts for

collaborators

9 papers

q-bio.NC2026

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…

cs.CR2026

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…

cs.ET2026

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…

cs.NE2025

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…

cs.AR2025

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…

cs.ET2024★ 1 cited

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…

cs.AR2024

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…

cs.ET2024

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…

cs.AR2024

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,…