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 …

Hardware realization of artificial intelligence (AI) requires new design styles and even
underlying technologies than those used in traditional digital processors or logic circuits …

Analog hardware accelerators, which perform computation within a dense memory array,
have the potential to overcome the major bottlenecks faced by digital hardware for data …

Memristor based neuromorphic computing systems give alternative solutions to boost the
computing energy efficiency of Neural Network (NN) algorithms. Because of the large-scale …

In the age of artificial intelligence (AI), the huge data movements between memory and
computing units become the bottleneck of von Neumann architectures, ie, the “memory wall” …

Matrix-Vector Multiplications (MVMs) represent a heavy workload for both training and
inference in Deep Neural Networks (DNNs) applications. Analog In-memory Computing …

Specialized accelerators have recently garnered attention as a method to reduce the power
consumption of neural network inference. A promising category of accelerators utilizes …

Self-attention-based transformers have outperformed recurrent and convolutional neural
networks (RNN/CNNs) in many applications. Despite the effectiveness, calculating self …

As its core computation, a self-attention mechanism gauges pairwise correlations across the
entire input sequence. Despite favorable performance, calculating pairwise correlations is …

Resistive random-access memory (ReRAM) is a promising technology for designing
hardware accelerators for deep neural network (DNN) inferencing. However, stochastic …