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

Nearest neighbor (NN) search is an essential operation in many applications, such as
one/few-shot learning and image classification. As such, fast and low-energy hardware …

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

Content-addressable memories (CAMs) are widely used for data-centric applications where
one must search for data patterns. CMOS CAMs can incur large areas and, hence, power …

To overcome the performance degradation in hardware neural networks (NNs) with non-
ideal synapse devices, we proposed a novel neuromorphic architecture with both TiO x …

The ferroelectric-metal field-effect transistor with recessed channel (RC-FeMFET) is
proposed for one transistor dynamic random-access memory (1T-DRAM). Through …

In this article, we propose a hardware accelerator design using ferroelectric transistor
(FeFET)-based hybrid precision synapse (HPS) for deep neural network (DNN) on-chip …

We proposed 2D piezoelectric FET (PeFET)–based compute-enabled non-volatile memory
for ternary deep neural networks (DNNs). PeFETs hinge on ferroelectricity for bit storage and …

Flash memory-based computing-in-memory (CIM) architectures have gained popularity due
to their remarkable performance in various computation tasks of data processing, including …

Multiple research vectors represent possible paths to improved energy and performance
metrics at the application-level. There are active efforts with respect to emerging logic …