to meet the extensive computational load presented by the rapidly growing Machine
Learning (ML) and Artificial Intelligence (AI) algorithms such as Deep Neural Networks …

The staggering innovations and emergence of numerous deep learning (DL) applications
have forced researchers to reconsider hardware architecture to accommodate fast and …

Advanced computing systems have long been enablers for breakthroughs in artificial
intelligence (AI) and machine learning (ML) algorithms, either through sheer computational …

This paper presents a novel architecture for in-memory computation of binary neural network
(BNN) workloads based on STT-MRAM arrays. In the proposed architecture, BNN inputs are …

The slowing down of transistor scaling and explosive growth for intelligence computing
power emerge as the two driving factors for the study of novel devices and materials to …

To combat the high energy costs of moving data between main memory and the CPU, recent
works have proposed to perform processing-using-memory (PUM), a type of processing-in …

Computing in memory (CIM) is a promising candidate for high throughput and energy-
efficient data-driven applications, which mitigates the well-known memory bottleneck in Von …

Computing systems are widely used in medical diagnosis, climate prediction, autonomous
vehicles, etc. As the key part of electronics, the performance of computing systems is crucial …

This paper presents the first MRAM-based In-Memory-Computing (IMC) macro, implemented
as a 128-kb array in an advanced-node 22nm FD-SOI technology. The design maximizes …

Processing in memory (PIM) architecture, with its ability to perform ultra-low-latency parallel
processing, is regarded as a more suitable alternative to von Neumann computing …