4 citations · 5 across the 9 of their papers we have counts for
10 papers
Accuracy-Configurable Floating-Point Multiplier Design for SRAM-Based Compute-in-Memory
Yiqi Zhou, Junhao Lu, Jiale Yu +5
Digital Compute-in-Memory (DCiM) reduces data movement and has become a promising solution for energy-efficient edge AI. However, most existing DCiM frameworks still primarily targ…
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…
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…
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…
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…
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…
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…