In this work, we review Binarized Neural Networks (BNNs). BNNs are deep neural networks
that use binary values for activations and weights, instead of full precision values. With …

Content-addressable memory (CAM) is a massively parallel searching device that returns
the address of a given search input in one clock cycle. Field-programmable gate array …

Energy efficiency continues to be the core design challenge for artificial intelligence (AI)
hardware designers. In this paper, we propose a new AI hardware architecture targeting …

The k-nearest neighbor (kNN) is one of the most popular algorithms in machine learning
owing to its simplicity, versatility, and implementation viability without any assumptions about …

Recently, the Logic-in-Memory (LiM) concept has been widely studied in the literature. This
paradigm represents one of the most efficient ways to solve the limitations of a Von …

The accuracy of neural networks has greatly improved across various domains over the past
years. Their ever-increasing complexity, however, leads to prohibitively high energy …

A spectrum of emerging applications, including edge artificial intelligence, advocates the
precompute-and-search scheme with embedded small-size content addressable memory …

Content-Addressable Memory (CAM) circuits, distinguished by their ability to accelerate data
retrieval through a direct content-matching function, are increasingly crucial in the era of AI …

The increasing complexity of real-life applications demands constant improvements of
microprocessor systems. One of the most frequently adopted microprocessor design scheme …

In the hardware implementation of deep learning algorithms such as, convolutional neural
networks (CNNs) and binarized neural networks (BNNs), multiple dot products and …