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 …
An overview of in-memory processing with emerging non-volatile memory for data-intensive applications
The conventional von Neumann architecture has been revealed as a major performance
and energy bottleneck for rising data-intensive applications. The decade-old idea of …
and energy bottleneck for rising data-intensive applications. The decade-old idea of …
ReTransformer: ReRAM-based processing-in-memory architecture for transformer acceleration
Transformer has emerged as a popular deep neural network (DNN) model for Neural
Language Processing (NLP) applications and demonstrated excellent performance in …
Language Processing (NLP) applications and demonstrated excellent performance in …
Multi-objective optimization of ReRAM crossbars for robust DNN inferencing under stochastic noise
Resistive random-access memory (ReRAM) is a promising technology for designing
hardware accelerators for deep neural network (DNN) inferencing. However, stochastic …
hardware accelerators for deep neural network (DNN) inferencing. However, stochastic …
Near-memory computing on fpgas with 3d-stacked memories: Applications, architectures, and optimizations
The near-memory computing (NMC) paradigm has transpired as a promising method for
overcoming the memory wall challenges of future computing architectures. Modern systems …
overcoming the memory wall challenges of future computing architectures. Modern systems …
Reliable memristor-based neuromorphic design using variation-and defect-aware training
The memristor crossbar provides a unique opportunity to develop a neuromorphic
computing system (NCS) with high scalability and energy efficiency. However, the reliability …
computing system (NCS) with high scalability and energy efficiency. However, the reliability …
Embedding error correction into crossbars for reliable matrix vector multiplication using emerging devices
Emerging memory devices are an attractive choice for implementing very energy-efficient in-
situ matrix-vector multiplication (MVM) for use in intelligent edge platforms. Despite their …
situ matrix-vector multiplication (MVM) for use in intelligent edge platforms. Despite their …
Reliability enhancement of inverter-based memristor crossbar neural networks using mathematical analysis of circuit non-idealities
In this paper, the sensitivity of the neural network (NN) outputs to device parameter
uncertainties (non-idealities) in inverter-based memristor (IM) crossbar neuromorphic …
uncertainties (non-idealities) in inverter-based memristor (IM) crossbar neuromorphic …
Power-aware training for energy-efficient printed neuromorphic circuits
There is an increasing demand for next-generation flexible electronics in emerging low-cost
applications such as smart packaging and smart bandages, where conventional silicon …
applications such as smart packaging and smart bandages, where conventional silicon …
Improving the robustness and efficiency of PIM-based architecture by SW/HW co-design
Processing-in-memory (PIM) based architecture shows great potential to process several
emerging artificial intelligence workloads, including vision and language models. Cross …
emerging artificial intelligence workloads, including vision and language models. Cross …