Brain-inspired computing is a growing and interdisciplinary area of research that
investigates how the computational principles of the biological brain can be translated into …

A fast and efficient search function across the database has been a core component for a
number of data-intensive tasks in machine learning, IoT applications, and inference …

In this paper, we present a temperature and variability-aware Verilog-A-based compact
model for simulating Ferroelectric FET. The model captures the rich physics of ferroelectric …

The separation of computing units and memory in the computer architecture mandates
energy-intensive data transfers creating the von Neumann bottleneck. This bottleneck is …

The Ferroelectric Field-Effect Transistor (FeFET) is an emerging Non-Volatile Memory (NVM)
technology enabling novel data-centric architectures that go far beyond von Neumann …

Brain-inspired hyperdimensional computing (HDC) is continuously gaining remarkable
attention. It is a promising alternative to traditional machine-learning approaches due to its …

Breakthroughs in Deep neural networks (DNNs) steadily bring new innovations that
substantially improve our daily life. However, DNNs overwhelm our existing computer …

Specialized hardware accelerators beyond von-Neumann, that offer processing capability in
where the data resides without moving it, become inevitable in data-centric computing …

Hyperdimensional computing (HDC) is an emerging learning paradigm that has gained a lot
of attention due to its ability to train with fewer data, lightweight implementation, and …

The rapidly increasing volume and complexity of data is demanding the relentless scaling of
computing power. With transistor feature size approaching physical limits, the benefits that …