Hyperdimensional computing (HDC) is a brain-inspired computational framework that relies
on long hypervectors (HVs) for learning. In HDC, computational operations consist of simple …

Hafnium oxide-based ferroelectric field-effect-transistors (FeFET), which combine super-
steep logical switching and low power non-volatile memory functions, have significant …

Content addressable memory (CAM) is widely used for data-centric computing for its
massive parallelism and pattern matching capability. Though the CAM density has been …

In-memory computing is an emerging computing paradigm that overcomes the limitations of
exiting Von-Neumann computing architectures such as the memory-wall bottleneck. In such …

Pattern searches, a key operation in many data analytic applications, often deal with data
represented by multiple states per dimension. However, hash tables, a common software …

Nearest neighbor (NN) search computations are at the core of many applications such as
few-shot learning, classification, and hyperdimensional computing. As such, efficient …

Content addressable memory (CAM) is widely used in associative search tasks due to its
parallel pattern matching capability. As more complex and data‐intensive tasks emerge, it is …

Advances in graph neural network (GNN)-based algorithms enable machine learning on
relational data. GNNs are computationally demanding since they rely upon backpropagation …

In a number of machine learning models, an input query is searched across the trained class
vectors to find the closest feature class vector in cosine similarity metric. However …

This is the first work to present a reliable application for highly scaled (down to merely 3nm),
multi-bit Ferroelectric FET (FeFET) technology. FeFET is one of the up-and-coming …