As data movement operations and power-budget become key bottlenecks in the design of
computing systems, the interest in unconventional approaches such as processing-in …

The Internet of Things (IoT) has led to the emergence of big data. Processing this amount of
data poses a challenge for current computing systems. PIM enables in-place computation …

In this paper, we propose MRIMA, as a novel magnetic RAM (MRAM)-based in-memory
accelerator for nonvolatile, flexible, and efficient in-memory computing. MRIMA transforms …

Data transfer between a processor and memory frequently represents a bottleneck with
respect to improving application-level performance. Computing in memory (CiM), where …

Due to the amount of data involved in emerging deep learning and big data applications,
operations related to data movement have quickly become a bottleneck. Data-centric …

Recent years have witnessed a rapid growth in the domain of Internet of Things (IoT). This
network of billions of devices generates and exchanges huge amount of data. The limited …

In this paper, we propose PIMA-Logic, as a novel Processing-in-Memory Architecture for
highly flexible and efficient Logic computation. Insteadof integrating complex logic units in …

Neural networks (NNs) have shown great ability to process emerging applications such as
speech recognition, language recognition, image classification, video segmentation, and …

In this work, we present GraphS architecture, which transforms current Spin Orbit Torque
Magnetic Random Access Memory (SOT-MRAM) to massively parallel computational units …

In recent years, we are witnessing a trend toward in-memory computing for future
generations of computers that differs from traditional von-Neumann architecture in which …