CORDIS Project
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This project aims to develop an in-memory computing accelerator designed for deep neural network inference, significantly reducing latency and power consumption. By utilizing resistive memory devices, it seeks to streamline data processing and enhance efficiency in various computing environments.
Deep neural networks (DNNs), loosely inspired by biological neural networks, consist of parallel processing units called neurons interconnected by plastic synapses.
By tuning the weights of these interconnections, these networks are able to perform certain cognitive tasks remarkably well. DNNs are being deployed all the way from cloud data centers to edge servers and even end devices and is projected to be a tens of billion Euro-market just for semiconductor companies in the next few years.
Ther…
IBM RESEARCH GMBH
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