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

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 …

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 …

In-memory computing is an attractive solution for reducing power consumption and memory
access latency cost by performing certain computations directly in memory without reading …

Similarity search, that is, finding similar items in massive data, is a fundamental computing
problem in many fields such as data mining and information retrieval. However, for large …

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

As a promising alternative to the von Neumann architecture, in-memory computing holds the
promise of delivering a high computing capacity while consuming low power. In this paper …

Hyperdimensional Computing (HDC) is an emerging computational framework that mimics
important brain functions by operating over high-dimensional vectors, called hypervectors …

In this work, we identify the potential challenges of ambipolar ferroelectric field effect
transistor (FeFET) in building a single transistor CAM array to perform parallel hamming …