The current challenges and future perspectives of the in-memory computing utilizing flexible and stretchable memristive arrays are presented. These efforts aim to accelerate the development of ...

One notable example is in-memory computing based on crossbar arrays of non-volatile memories that execute, in an analogue manner, multiply–accumulate operations prevalent in artificial neural networks ...

“Conventional resistive crossbar array for in-memory computing suffers from high static current/power, serious IR drop, and sneak paths. In contrast, the “capacitive” crossbar array that harnesses ...

Abstract: Computing-In-Memory (CIM) is widely applied in neural networks due to its unique capability to perform multiply-and-accumulate operations within a circuit array. This process directly ...

Non-volatile computing-in-memory (nvCIM) is a novel architecture used for deep neural networks (DNNs) because it can reduce the movement of data between computing units and memory units. As sparsity ...

A generic architecture for analog in-memory computing. Unlike traditional memory operations, in-memory computation does not happen at the granularity of a single memory element. Instead, it’s a ...

The team is now working to scale up from a single memory cell to a large-scale memory array which can support even more data for computing applications. They note in the article that the ...

A multi-level breakthrough in optical computing Date: October 23, 2024 Source: University of Pittsburgh Summary: Until now, researchers have been limited in developing photonic memory for AI ...