The performance gap between memory and CPU has grown exponentially. To bridge this
gap, hardware architects have proposed near-memory computing (also called processing-in …

Machine Learning (ML) is making a strong resurgence in tune with the massive generation
of unstructured data which in turn requires massive computational resources. Due to the …

DRAM latency is a major bottleneck for many applications in modern computing systems. In
this work, we rigorously characterize the effects of reducing DRAM access latency on 282 …

Processing-using-DRAM (PuD) is an emerging paradigm that leverages the analog
operational properties of DRAM circuitry to enable massively parallel in-DRAM computation …

Poor DRAM technology scaling over the course of many years has caused DRAM-based
main memory to increasingly become a larger system bottleneck. A major reason for the …

Spiking neural networks (SNNs) have shown a potential for having low energy with
unsupervised learning capabilities due to their biologically-inspired computation. However …

DRAM is the prevalent main memory technology, but its long access latency can limit the
performance of many workloads. Although prior works provide DRAM designs that reduce …

We empirically evaluate an undervolting technique, ie, underscaling the circuit supply
voltage below the nominal level, to improve the power-efficiency of Convolutional Neural …

RowHammer is a DRAM vulnerability that can cause bit errors in a victim DRAM row by just
accessing its neighboring DRAM rows at a high-enough rate. Recent studies demonstrate …

DRAM is the dominant main memory technology used in modern computing systems.
Computing systems implement a memory controller that interfaces with DRAM via DRAM …