Deep Neural Networks (DNNs) are inherently computation-intensive and also power-
hungry. Hardware accelerators such as Field Programmable Gate Arrays (FPGAs) are a …

DRAM scalability is becoming a limiting factor to the available memory capacity in consumer
devices. As a potential solution, manufacturers have introduced emerging non-volatile …

Improvements in main memory storage density are primarily driven by process technology
scaling, which negatively impacts reliability by exacerbating various circuit-level error …

We present the first rigorous security, performance, energy, and cost analyses of the state-of-
the-art on-DRAM-die read disturbance mitigation method, Per Row Activation Counting …

There are two major sources of inefficiency in computing systems that use modern DRAM
devices as main memory. First, due to coarse-grained data transfers (size of a cache block …

Deep neural network (deepnet) applications play a crucial role in safety-critical systems
such as autonomous vehicles (AVs). An AV must drive safely towards its destination …

DRAM vendors do not disclose the architecture of the sense amplifiers deployed in their
chips. Unfortunately, this hinders academic research that focuses on studying or improving …

To employ a Convolutional Neural Network (CNN) in an energy-constrained embedded
system, it is critical for the CNN implementation to be highly energy efficient. Many recent …

The combination of neuromorphic computing (NC) and 3-D integrated circuits-the 3-D
stacking neuromorphic system can be the most advanced architecture that inherits the …

Computing is bottlenecked by data. Large amounts of application data overwhelm storage
capability, communication capability, and computation capability of the modern machines …