Research progress on memristor: From synapses to computing systems
As the limits of transistor technology are approached, feature size in integrated circuit
transistors has been reduced very near to the minimum physically-realizable channel length …
transistors has been reduced very near to the minimum physically-realizable channel length …
Endurance-aware mapping of spiking neural networks to neuromorphic hardware
Neuromorphic computing systems are embracing memristors to implement high density and
low power synaptic storage as crossbar arrays in hardware. These systems are energy …
low power synaptic storage as crossbar arrays in hardware. These systems are energy …
Analog neural computing with super-resolution memristor crossbars
Memristor crossbar arrays are used in a wide range of in-memory and neuromorphic
computing applications. However, memristor devices suffer from non-idealities that result in …
computing applications. However, memristor devices suffer from non-idealities that result in …
SWAP: A server-scale communication-aware chiplet-based manycore PIM accelerator
Processing-in-memory (PIM) is a promising technique to accelerate deep learning (DL)
workloads. Emerging DL workloads (eg, ResNet with 152 layers) consist of millions of …
workloads. Emerging DL workloads (eg, ResNet with 152 layers) consist of millions of …
On endurance of processing in (nonvolatile) memory
Processing-in-Memory (PIM) architectures have gained popularity due to their ability to
alleviate the memory wall by performing large numbers of operations within the memory …
alleviate the memory wall by performing large numbers of operations within the memory …
Accelerating large-scale graph neural network training on crossbar diet
Resistive random-access memory (ReRAM)-based manycore architectures enable
acceleration of graph neural network (GNN) inference and training. GNNs exhibit …
acceleration of graph neural network (GNN) inference and training. GNNs exhibit …
Essence: Exploiting structured stochastic gradient pruning for endurance-aware reram-based in-memory training systems
Processing-in-memory (PIM) enables energy-efficient deployment of convolutional neural
networks (CNNs) from edge to cloud. Resistive random-access memory (ReRAM) is one of …
networks (CNNs) from edge to cloud. Resistive random-access memory (ReRAM) is one of …
Data Pruning-enabled High Performance and Reliable Graph Neural Network Training on ReRAM-based Processing-in-Memory Accelerators
Graph Neural Networks (GNNs) have achieved remarkable accuracy in cognitive tasks such
as predictive analytics on graph-structured data. Hence, they have become very popular in …
as predictive analytics on graph-structured data. Hence, they have become very popular in …
Accelerating Graph Neural Network Training on ReRAM-Based PIM Architectures via Graph and Model Pruning
Graph neural networks (GNNs) are used for predictive analytics on graph-structured data,
and they have become very popular in diverse real-world applications. Resistive random …
and they have become very popular in diverse real-world applications. Resistive random …
Realizing Extreme Endurance Through Fault-aware Wear Leveling and Improved Tolerance
Phase-change memory (PCM) and resistive memory (RRAM) are promising alternatives to
traditional memory technologies. However, both PCM and RRAM suffer from limited write …
traditional memory technologies. However, both PCM and RRAM suffer from limited write …