Memory-centric neuromorphic computing for unstructured data processing

Novel synaptic memory device for neuromorphic computing

This report discusses the electrical characteristics of two-terminal synaptic memory devices capable of demonstrating an analog change in conductance in response to the varying amplitude and pulse-width of the applied signal. The devices are based on Mn doped HfO₂ material. The mechanism behind reconfiguration was studied and a unified model is presented to explain the underlying device physics...

Memory system architecture for the data centric computing

Photonic Neuromorphic Signal Processing and Computing

There has been a recent explosion of interest in spiking neural networks (SNNs), which code information as spikes or events in time. Spike encoding iswidely accepted as the information medium underlying the brain, but it has also inspired a new generation of neuromorphic hardware. Although electronics can match biological time scales and exceed them, they eventually reach a bandwidth fan-in tra...

An Efficient Resource Allocation for Processing Healthcare Data in the Cloud Computing Environment

Nowadays, processing large-media healthcare data in the cloud has become an effective way of satisfying the medical userschr('39') QoS (quality of service) demands. Providing healthcare for the community is a complex activity that relies heavily on information processing. Such processing can be very costly for organizations. However, processing healthcare data in cloud has become an effective s...

DataScalar: A memory-centric approach to computing

Commodity microprocessors contain more on-chip memory with each successive generation, and will contain tens of megabytes within the decade. We describe a novel architecture that runs an unmodified uniprocessor program across multiple nodes, each of which contains a processor tightly integrated with a sizable memory. The execution of instructions is replicated, while the access of operands is d...