Computing in memory (CIM) could be used to overcome the von Neumann bottleneck and to
provide sustainable improvements in computing throughput and energy efficiency …

Artificial intelligence (AI) and machine learning (ML) are revolutionizing many fields of study,
such as visual recognition, natural language processing, autonomous vehicles, and …

Compute-in-memory (CiM) is one promising solution to address the memory bottleneck
existing in traditional computing architectures. However, the tradeoff between energy …

In-memory computing (IMC) is an effective solution for energy-efficient artificial intelligence
applications. Analog IMC amortizes the power consumption of multiple sensing amplifiers …

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 …

This work introduces a digital SRAM-based near-memory compute macro for DNN
inference, improving on-chip weight memory capacity and area efficiency compared to state …

For the non-stop demands for a better and smarter society, the number of electronic devices
keeps increasing exponentially; and the computation power, communication data rate, smart …

In-memory computing (IMC) processors show significant energy and area efficiency for deep
neural network (DNN) processing [1–3]. As shown in Fig. 16.5. 1, despite promising macro …

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

Resistive random-access memory (RRAM) based non-volatile computing-in-memory
(nvCIM) has been regarded as a promising solution to enable efficient data-intensive …