In-memory computing (IMC) systems have great potential for accelerating data-intensive
tasks such as deep neural networks (DNNs). As DNN models are generally highly …

Memory latency and bandwidth are significant bottlenecks in designing in-memory indexes.
Processing-in-memory (PIM), an emerging hardware design approach, alleviates this …

Data movement between the memory system and computation units is one of the most
critical challenges in designing high performance and energy-efficient computing systems …

The adoption of processing-in-memory (PiM) architectures has been gaining momentum
because they provide high performance and low energy consumption by alleviating the data …

The performance of processor-centric von Neumann architectures is greatly hindered by
data movement between memory and processor, especially when encountering data …

This work presents the challenges and opportunities for developing computing-in-memory
(CIM) accelerators to support secure graph databases (GDB). First, we examine the …