This paper presents an efficient crossbar design and implementation intended for analog
compute-in-memory (ACiM) acceleration of artificial neural networks based on ferroelectric FET …

Transformer networks, driven by self-attention, are central to large language models. In
generative transformers, self-attention uses cache memory to store token projections, avoiding …

Emerging applications based on machine learning and Deep Neural Networks (DNNs) are
data-driven and memory-intensive. Hence, there is a recent shift from compute-centric …

Autonomous driving is disrupting conventional automotive development. Underlying reasons
include control unit consolidation, the use of components originally developed for the …

Modern and future AI-based automotive applications, such as autonomous driving, require
the efficient real-time processing of huge amounts of data from different sensors, like camera, …

Recently, we are witnessing a surge in DRAM-based Processing in Memory (PIM) publications
from academia and industry. The architectures and design techniques proposed in these …

Energy consumption is one of the major challenges for the advanced System on Chips (SoC).
This is addressed by adopting heterogeneous and approximate computing techniques. …

Many advanced neural network inference engines are bounded by the available memory
bandwidth. The conventional approach to address this issue is to employ high bandwidth …

DRAM technology is scaling aggressively that results in high leakage power, worse data
retention time behavior, and large process variations. Due to these process variations, vendors …

In this paper, we present a new communication theoretic channel model for Dynamic Random
Access Memory (DRAM) retention errors, that relies on the fully asymmetric retention error …