In recent years, the limits of the multicore approach emerged in the so-called “dark silicon”
issue and diminishing returns of an ever-increasing core count. Hardware manufacturers …

As general-purpose processors have hit the power wall and chip fabrication cost escalates
alarmingly, coarse-grained reconfigurable architectures (CGRAs) are attracting increasing …

Emerging applications such as deep neural network demand high off-chip memory
bandwidth. However, under stringent physical constraints of chip packages and system …

Modern computing systems are overwhelmingly designed to move data to computation. This
design choice goes directly against at least three key trends in computing that cause …

Many important applications trigger bulk bitwise operations, ie, bitwise operations on large
bit vectors. In fact, recent works design techniques that exploit fast bulk bitwise operations to …

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 …

The monolithic server model where a server is the unit of deployment, operation, and failure
is meeting its limits in the face of several recent hardware and application trends. To improve …

Processing-in-memory (PIM) is a promising solution to address the" memory wall"
challenges for future computer systems. Prior proposed PIM architectures put additional …

The high accuracy of deep neural networks (NNs) has led to the development of NN
accelerators that improve performance by two orders of magnitude. However, scaling these …

Many modern workloads, such as neural networks, databases, and graph processing, are
fundamentally memory-bound. For such workloads, the data movement between main …