Few-shot learning (FSL) is an important and topical problem in computer vision that has
motivated extensive research into numerous methods spanning from sophisticated meta …

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

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 …

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 …

Associative access is widely used in fundamental microarchitectural components, such as
caches and TLBs. However, associative (or content addressable) memories (CAMs) have …

In this paper, we combine a Content-Addressable Memory (CAM) encoding scheme
previously proposed for analog electronic CAMs (E-CAMs) with optical multiplexing …

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

Content addressable memory (CAM) has been employed in various data-intensive tasks for
its parallel pattern-matching capability. To enhance the density and efficiency of CAMs …

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 …

Content addressable memories (CAMs), a special-purpose in-memory computing (IMC) unit,
support parallel searches directly in memory. There are growing interests in CAMs for data …