Compute memories are memory arrays augmented with dedicated logic to support
arithmetic. They support the efficient execution of data-centric computing patterns, such as …

The ever-increasing computational complexity and energy consumption of today's
applications, such as machine learning (ML) algorithms, not only strain the capabilities of the …

The fusion of sensors with AI at the edge enables energy-efficient and real-time monitoring
and detection. However, very few hardware implementations of edge AI in microwave …

Deep neural network (DNN) is becoming pervasive in today's applications with intelligent
autonomy. Nonetheless, the ever-increasing complexity of modern DNN models caused …

Hyperdimensional computing (HDC) is emerging as a promising AI approach that can
effectively target TinyML applications thanks to its lightweight computing and memory …

When arranged in a crossbar configuration, resistive memory devices can be used to
execute MVM, the most dominant operation of many ML algorithms, in constant time …

Machine learning algorithms such as Convolutional Neural Networks (CNNs) are
characterized by high robustness towards quantization, supporting small-bitwidth fixed-point …

In-Memory Computing: The Emerging Computing Topic in the Post-von Neumann Era Page
1 SPOTLIGHT ON TRANSACTIONS 4 COMPUTER PUBLISHED BY THE IEEE COMPUTER …

Driven by the demand for real-time processing and the need to minimize latency in AI
algorithms, edge computing has experienced remarkable progress. Decision-making AI …