Data movement between the processing units and the memory in traditional von Neumann
architecture is creating the" memory wall" problem. To bridge the gap, two approaches, the …

In this paper, we propose ReDRAM, as a reconfigurable DRAM-based processing-in-
memory (PIM) accelerator, which transforms current DRAM architecture to massively parallel …

Eyeriss is an accelerator for state-of-the-art deep convolutional neural networks (CNNs). It
optimizes for the energy efficiency of the entire system, including the accelerator chip and off …

Image processing is becoming an increasingly important domain for many applications on
workstations and the datacenter that require accelerators for high performance and energy …

Memory-centric architecture, which bridges the gap between compute and memory, is
considered as a promising solution to tackle the memory wall and the power wall. Such …

Modern Convolutional Neural Networks (CNNs) are computation and memory intensive.
Thus it is crucial to develop hardware accelerators to achieve high performance as well as …

The energy efficiency of accelerating hundreds of MB-large deep neural networks (DNNs) in
a mobile environment is less than that of a server-class big chip accelerator because of the …

Data movement between main memory and the processor is a significant contributor to the
execution time and energy consumption of memory-intensive applications. This data …

The performance of computer systems is often limited by the bandwidth of their memory
channels, but further increasing the bandwidth is challenging under the stringent pin and …

The growing size of convolutional neural networks (CNNs) requires large amounts of on-
chip storage. In many CNN accelerators, their limited on-chip memory capacity causes …