To use this for training, a grid of phase-change memory bits can be mapped to each layer of a neural network. A communication network, made of more traditional wiring, allows the neurons to ...

Instead of applying the two-step algorithmic memory retrieval on the rather static energy landscape of the original Hopfield network model, the researchers describe a dynamic, input-driven mechanism.

One prominent application of IMC is that of performing matrix-vector multiplication in (1) time complexity by mapping the synaptic weights of a neural-network layer to the devices of an IMC core.

"Compute-in-memory has been common practice in neuromorphic engineering since it ... NeuRRAM supports data-parallelism by mapping a layer in the neural network model onto multiple cores for ...

A new neural-network architecture developed by researchers at Google might solve one of the great challenges for large language models (LLMs): extending their memory at inference time without ...

Researchers at Tsinghua University, the China Mobile Research Institute, and Hebei University recently developed a new compute-in-memory chip for federated learning, which is based on memristors ...

University of California - Santa Barbara. "Energy and memory: A new neural network paradigm." ScienceDaily. ScienceDaily, 14 May 2025. <www.sciencedaily.com / releases / 2025 / 05 / 250514164320.htm>.

In doing so, not only did he provide a mathematical framework for understanding memory storage and retrieval in the human brain, he also developed one of the first recurrent artificial neural networks ...