Ferroelectric materials, the charge equivalent of magnets, have been the subject of
continued research interest since their discovery more than 100 years ago. The …

The Von-Neumann memory wall bottleneck that keeps expanding is mainly caused by the
frequent data transfer between the main memory and the processor. The processing in …

In this paper, we review the recent trends in parallel search and artificial intelligence (AI)
applications using emerging non-volatile ternary content addressable memory (TCAM) …

Transformer networks have outperformed recurrent and convolutional neural networks in
terms of accuracy in various sequential tasks. However, memory and compute bottlenecks …

Few-shot learning (FSL) is a promising meta-learning paradigm that trains classification
models on the fly with a few training samples. However, existing FSL classifiers are either …

Kernel functions are vital ingredients of several machine learning (ML) algorithms but often
incur substantial memory and computational costs. We introduce an approach to kernel …

Realizing compact and scalable Ising machines that are compatible with CMOS-process
technology is crucial to the effectiveness and practicality of using such hardware platforms …

Most of current processing-in-memory (PIM) architectures are application specific, that is,
they can only accelerate particular functions, eg, matrix-vector dot product for neural network …

Recommendation systems (RecSys) suggest items to users by predicting their preferences
based on historical data. Typical RecSys handle large embedding tables and many …

Emerging nonvolatile memory technologies are attracting interest from the system design
level to implement alternatives to conventional von-Neumann computing architectures. In …