Since its inception in 2014 [1], the modern version of in-memory computing (IMC) has
become an active area of research in integrated circuit design globally for realizing artificial …

In-memory computing (IMC) architectures have emerged as a compelling platform to
implement energy-efficient machine learning (ML) systems. However, today, the energy …

The data throughput in the von Neumann architecture-based computing system is limited by
its separated processing and memory structure, and the mismatching speed between the …

This paper presents a lookup table-based dot product approximation (LDA) for energy-
efficient convolutional neural network (CNN) inference as another computing-in-memory …

The computing-in-memory (CIM) technique is emerging with the evolvement of big data and
artificial intelligence (AI) application. The manuscript presents a systematic review of existing …

Computing-in-memory (CIM) is an attractive approach for energy-efficient neural network
(NN) processors. Attention mechanisms shows great performance in NLP and CV by …

In this paper, two types of low power and energy efficient neural network (NN) inference
processors are introduced. First, an energy efficient accelerator for convolutional neural …

Transformer models shows state-of-the-art results in natural language processing and
computer vision, leveraging a multi-headed self-attention mechanism. In each head, the …