Abstract Recently, Vision Transformer (ViT) has continuously established new milestones in
the computer vision field, while the high computation and memory cost makes its …

Quantum noise is the key challenge in Noisy Intermediate-Scale Quantum (NISQ)
computers. Previous work for mitigating noise has primarily focused on gate-level or pulse …

Recent work demonstrated the promise of using resistive random access memory (ReRAM)
as an emerging technology to perform inherently parallel analog domain in-situ matrix …

As real-world graphs expand in size, larger GNN models with billions of parameters are
deployed. High parameter count in such models makes training and inference on graphs …

The efficiency of machine intelligence techniques has improved noticeably in the embedded
application domains thanks to the dedicated hardware accelerators for deep neural …

Neuromorphic computing uses spiking neuron network models to solve machine learning
problems in a more energy-efficient way when compared to conventional artificial neural …

We propose a design methodology to facilitate fault tolerance of deep learning models. First,
we implement a many-core fault-tolerant neuromorphic hardware design, where neuron and …

Stochastic rounding is a critical technique used in low-precision deep neural networks
(DNNs) training to ensure good model accuracy. However, it requires a large number of …

Neuromorphic computing utilizes spiking neural networks (SNNs) to offer power/energy-
efficient solutions for complex machine-learning problems in hardware. However, neural …

The crossbar structure of the nonvolatile memory enables highly parallel and energy-
efficient analog matrix-vector-multiply (MVM) operations. To exploit its efficiency, existing …