Bulk bitwise operations, ie, bitwise operations on large bit vectors, are prevalent in a wide
range of important application domains, including databases, graph processing, genome …

Processing-using-DRAM (PUD) is a processing-in-memory (PIM) approach that uses a
DRAM array's massive internal parallelism to execute very-wide (eg, 16,384-262,144-bit …

The use of deep learning (DL) to real-world applications, such as computer vision, speech
recognition, and robotics, has become ubiquitous. This can be largely attributed to a virtuous …

Metagenomics, the study of the genome sequences of diverse organisms in a common
environment, has led to significant advances in many fields. Since the species present in a …

Memory-centric computing aims to enable computation capability in and near all places
where data is generated and stored. As such, it can greatly reduce the large negative …

As the model size of Deep Neural Networks (DNN) is extremely expanding, especially in
Natural Language Processing (NLP) field, it is not suitable for performing AI application on …

The processing of large amounts of data requires a high energy efficiency and fast
processing time for high-performance computing systems. However, conventional von …

DNNs deployed on analog processing in memory (PIM) architectures are subject to
fabrication-time variability. We developed a new joint variability-and quantization-aware …

Metagenomics has led to significant advancements in many fields. Metagenomic analysis
commonly involves the key tasks of determining the species present in a sample and their …

Compute-in-memory (CiM) has emerged as a compelling solution to alleviate high data
movement costs in von Neumann machines. CiM can perform massively parallel general …