This comprehensive review explores the advancements in processing-in-memory (PIM)
techniques and chiplet-based architectures for deep neural networks (DNNs). It addresses …

In this paper, we propose BioHD, a novel genomic sequence searching platform based on
Hyper-Dimensional Computing (HDC) for hardware-friendly computation. BioHD transforms …

Today's applications generate a large amount of data that need to be processed by learning
algorithms. In practice, the majority of the data are not associated with any labels …

As we approach the end of Moore's law, many alternative devices are being explored to
satisfy the performance requirements of modern integrated circuits. At the same time, the …

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 …

Faster and cheaper computers have been constantly demanding technological and
architectural improvements. However, current technology is suffering from three technology …

Processing-In-Memory (PIM) has emerged as a high-performance and energy-efficient
computing paradigm for accelerating convolutional neural network (CNN) applications …

To overcome the “von Neumann bottleneck,” methods to compute in memory are being
researched in many emerging memory technologies, including resistive RAMs (ReRAMs) …

In-memory computing using emerging technologies such as resistive random-access
memory (ReRAM) addresses the 'von Neumann bottleneck'and strengthens the present …

Today's high performance computing (HPC) systems are limited by the expensive data
movement between processing and memory units. An emerging solution strategy is to …