In computer architecture studies, simulators are crucial for design verification, reducing
research and development time and ensuring the high accuracy of verification results …

Memory latency and bandwidth are significant bottlenecks in designing in-memory indexes.
Processing-in-memory (PIM), an emerging hardware design approach, alleviates this …

Resistive random-access memory (ReRAM)-based processing-in-memory (PIM)
architectures are used extensively to accelerate inferencing/training with convolutional …

Training Deep Neural Networks (DNNs) requires a large number of operations, among
which matrix-vector multiplies (MVMs), often of high dimensionality, dominate. In-Memory …

Training deep/convolutional neural networks (DNNs/CNNs) requires a large amount of
memory and iterative computation, which necessitates speedup and energy reduction …

The increasing interest in TinyML, ie, near-sensor machine learning on power budgets of a
few tens of mW, is currently pushing toward enabling TinyML-class training as opposed to …

Processing-in-Memory (PIM) is an emerging computing architecture that has gained
significant attention in recent times. It aims to maximize data movement efficiency by moving …

Over the past few years, on-device learning (ODL) has become an integral aspect of the
success of edge devices that embrace machine learning (ML) since it plays a crucial role in …

Recently, frequent data movement between computing units and memory during floating-
point arithmetic has become a major problem for scientific computing. Computing-in-memory …

Deep neural networks (DNNs) have recently gained significant prominence in various real-
world applications such as image recognition, natural language processing, and …