In recent years, deep learning has gained unprecedented success in various domains, the
key of the success is the larger and deeper deep neural networks (DNNs) that achieved very …

Convolution neural networks (CNNs) are the heart of deep learning applications. Recent
works PRIME [1] and ISAAC [2] demonstrated the promise of using resistive random access …

Processing-In-Memory (PIM) is an effective technique that reduces data movements by
integrating processing units within memory. The recent advance of “big data” and 3D stacking …

Graph processing recently received intensive interests in light of a wide range of needs to
understand relationships. It is well-known for the poor locality and high memory bandwidth …

With the recent advance of wearable devices and Internet of Things (IoTs), it becomes
attractive to implement the Deep Convolutional Neural Networks (DCNNs) in embedded and …

Large-scale deep neural networks (DNNs) are both compute and memory intensive. As the
size of DNNs continues to grow, it is critical to improve the energy efficiency and performance …

With the emergence of a spectrum of high-end mobile devices, many applications that
formerly required desktop-level computation capability are being transferred to these devices. …

Emerging non-volatile memory (NVM) offers non-volatility, byte-addressability and fast
access at the same time. To make the best use of these properties, it has been shown by …

Model compression is an important technique to facilitate efficient embedded and hardware
implementations of deep neural networks (DNNs), a number of prior works are dedicated to …

Processing-In-Memory (PIM) architectures based on recent technology advances (eg,
Hybrid Memory Cube) demonstrate great potential for graph processing. However, existing …