Multi-ported memories are widely used in many applications, such as for high-speed and
high-performance parallel computations. While conventional SRAM-based memory macros …

In the era of climate change, and with the rapid depletion of fossil resources, efficient and
sustainable transportation systems, such as hybrid electric vehicles (HEVs), are becoming …

Efficient battery technology is imperative for the adoption of clean energy automotive
solutions. In addition, efficient battery technology extends the useful life of the battery as well …

Neuromorphic computing, inspired by biological neural networks, has emerged as a
promising approach for solving complex machine learning tasks with greater efficiency and …

In the realm of signal processing, frequency and spectrum detection are fundamental tasks
that can be computationally intensive. This project leverages the power of FPGAs to perform …

Abstract The deployment of Linear Kalman Filters with high computational performance to
the field of multi-phase power converters has yet to be thoroughly explored in current …

In the era of smart and autonomous systems, machine learning is becoming the cornerstone
of these systems. Machine learning, a subset of artificial intelligence, is being incorporated …

Within the last decade, there has been a rapid growth in utilization of machine learning in
various fields and applications. Techniques and algorithms employed for machine learning …

In recent years, several efforts have explored the construction of multi-ported RAMs using on-
chip BRAMs within FPGAs to cater to real-time and data-intensive applications. These …

In the era of IoT, a massive amount of data will be generated from various sensors and
corresponding IoT devices. Density-based Ant Colony Stream Clustering (ACSC) is one of …