Programming memristor arrays with arbitrarily high precision for analog computing
In-memory computing represents an effective method for modeling complex physical
systems that are typically challenging for conventional computing architectures but has been …
systems that are typically challenging for conventional computing architectures but has been …
TEFLON: Thermally Efficient Dataflow-Aware 3D NoC for Accelerating CNN Inferencing on Manycore PIM Architectures
Resistive random-access memory (ReRAM)-based processing-in-memory (PIM)
architectures are used extensively to accelerate inferencing/training with convolutional …
architectures are used extensively to accelerate inferencing/training with convolutional …
SAL: Optimizing the Dataflow of Spin-based Architectures for Lightweight Neural Networks
As the Convolutional Neural Network (CNN) goes deeper and more complex, the network
becomes memory-intensive and computation-intensive. To address this issue, the …
becomes memory-intensive and computation-intensive. To address this issue, the …
Static Scheduling of Weight Programming for DNN Acceleration with Resource Constrained PIM
Most existing architectural studies on ReRAM-based processing-in-memory (PIM) DNN
accelerators assume that all weights of the DNN can be mapped to the crossbar at once …
accelerators assume that all weights of the DNN can be mapped to the crossbar at once …
Energy-Efficient DNN Inferencing on ReRAM-Based PIM Accelerators Using Heterogeneous Operation Units
Operation Unit (OU)-based configurations enable the design of energy-efficient and reliable
ReRAM crossbar-based Processing-In-Memory (PIM) architectures for Deep Neural Network …
ReRAM crossbar-based Processing-In-Memory (PIM) architectures for Deep Neural Network …
ChainPIM: A ReRAM-Based Processing-in-Memory Accelerator for HGNNs via Chain Structure
W Xiao, J Wang, D Chen, C Shi, X Ling… - … on Computer-Aided …, 2025 - ieeexplore.ieee.org
Heterogeneous graph neural networks (HGNNs) have recently demonstrated significant
advantages of capturing powerful structural and semantic information in heterogeneous …
advantages of capturing powerful structural and semantic information in heterogeneous …
Trade-off Performance and Energy Efficiency by Optimizing the Data Flow for PIM Architectures
The Processing-In-Memory (PIM) architecture becomes a promising candidate for deep
learning accelerators by integrating computation and memory. Most PIM-based studies …
learning accelerators by integrating computation and memory. Most PIM-based studies …
ReShare: A Resource-Efficient Weight Pattern Sharing Scheme for Memristive DNN Accelerators
S Hong, YC Chung - 2024 IEEE International Symposium on …, 2024 - ieeexplore.ieee.org
Memristor crossbar-based computing-in-memory (CIM) has garnered significant attention for
accelerating deep neural networks (DNNs). Although practical operation unit (OU)-based …
accelerating deep neural networks (DNNs). Although practical operation unit (OU)-based …
Design of The Ultra-Low-Power Driven VMM Configurations for μW Scale IoT Devices
K Takano, T Yajima, S Kawakami - 2023 IEEE 16th …, 2023 - ieeexplore.ieee.org
Operating IoT devices by supplying power from an energy harvester and installing AI
accelerators in IoT devices are required. Nevertheless, conventionally selected architectures …
accelerators in IoT devices are required. Nevertheless, conventionally selected architectures …