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 …

Privacy has rapidly become a major concern/design consideration. Homomorphic
Encryption (HE) and Garbled Circuits (GC) are privacy-preserving techniques that support …

Ternary content addressable memory (TCAM), widely used in network routers and high-
associativity caches, is gaining popularity in machine learning and data-analytic …

Memory-augmented neural network (MANN) has received increasing attention as a
promising approach to achieve lifelong on-device learning, of which implementation of the …

There is great interest in “end-to-end” analysis that captures how innovation at the materials,
device, and/or archi-tectural levels will impact figures of merit at the application-level …

With ever increasing depth and width in deep neural networks to achieve state-of-the-art
performance, deep learning computation has significantly grown, and dot-products remain …

Memory-augmented neural networks (MANNs) in-corporate external memories to address
the significant issue of catastrophic forgetting in few-shot learning applications. MANNs rely …

Content addressable memory (CAM) stands out as an efficient hardware solution for
memory-intensive search operations by supporting parallel computation in memory …

Content addressable memory (CAM) is a special-purpose search engine that can support
parallel search directly in memory. CAMs are of increasing interest for machine learning and …