Resistive RAM (ReRAM) is widely regarded as a promising platform for deep neural network
(DNN) acceleration. However, the ReRAM device suffers from severe thermal problems that …

Deep neural network (DNN) implementations are typically characterized by huge datasets
and concurrent computation, resulting in a demand for high memory bandwidth due to …

Resistive random-access memory (RRAM) has shown great potential for computing in
memory (CIM) to support the requirements of high memory bandwidth and low power in …

The Internet of Things (IoT) has led to the emergence of big data. Processing this data,
specially in learning algorithms, poses a challenge for current embedded computing …

Computing servers play a key role in the development and process of emerging compute-
intensive applications in recent years. However, they need to operate efficiently from an …

Achieving accurate multi-modal Deep Neural Networks (DNN) testing often requires
operating rich model parameters under limited computing and memory resources. The low …

The high speed, scalability, and parallelism offered by ReRAM crossbar arrays foster the
development of ReRAM-based next-generation AI accelerators. At the same time, the …

Recently, Processing In-Memory (PIM) has been shown as a promising solution to address
data movement issue in the current processors. However, today's PIM technologies are …

The higher speed, scalability and parallelism offered by ReRAM crossbar arrays foster
development of ReRAM-based next generation AI accelerators. At the same time, sensitivity …

Recent years have witnessed a rapid growth in the amount of generated data. Learning
algorithms, like hyperdimensional (HD) computing, promise to reduce the computation …