Temporal Correlation Detection Using Computational Phase Change Memory

Pdf Temporal Correlation Detection Using Computational Phase Change In this paper, we present an algorithm to detect temporal correlations between event based data streams using computational memory. In this paper, we present an algorithm whereby the crystallization dynamics of a type of resistive memory device, namely, phase change memory (pcm) devices, are exploited to implement a high level computational primitive.

Pdf Temporal Correlation Detection Using Computational Phase Change The computational memory engine exploits the accumulative property of ge 2 sb 2 te 5 phase change cells and wavelength division multiplexing property of optics in delivering fully parallelized and colocated temporal correlation detection computations. We present an experimental demonstration using one million phase change memory devices organized to perform a high level computational primitive by exploiting the crystallization dynamics. its result is imprinted in the conductance states of the memory devices. Here, we present a bioinspired computational primitive that utilizes an artificial spiking neuron equipped with plastic synapses to detect temporal correlations in data streams in an unsupervised manner. “we show how the crystallization dynamics of pcm devices can be exploited to detect statistical correlations between event based data streams. this can be applied in various fields such as the internet of things (iot), life sciences, networking, social networks, and large scientific experiments.

Correlation Detection Realized Using Phase Change Based Computational Here, we present a bioinspired computational primitive that utilizes an artificial spiking neuron equipped with plastic synapses to detect temporal correlations in data streams in an unsupervised manner. “we show how the crystallization dynamics of pcm devices can be exploited to detect statistical correlations between event based data streams. this can be applied in various fields such as the internet of things (iot), life sciences, networking, social networks, and large scientific experiments. We present an experimental demonstration using one million phase change memory devices organized to perform a high level computational primitive by exploiting the crystallization dynamics. Here we present a large scale experimental demonstration using one million phase change memory devices organized to perform a high level computational primitive by exploiting the. Bibliographic details on temporal correlation detection using computational phase change memory. Here we present a large scale experimental demonstration using one million phase change memory devices organized to perform a high level computational primitive by exploiting the crystallization dynamics.

Computational Phase Change Memory Pc Perspective We present an experimental demonstration using one million phase change memory devices organized to perform a high level computational primitive by exploiting the crystallization dynamics. Here we present a large scale experimental demonstration using one million phase change memory devices organized to perform a high level computational primitive by exploiting the. Bibliographic details on temporal correlation detection using computational phase change memory. Here we present a large scale experimental demonstration using one million phase change memory devices organized to perform a high level computational primitive by exploiting the crystallization dynamics.

Accurate Deep Neural Network Inference Using Computational Phase Change Bibliographic details on temporal correlation detection using computational phase change memory. Here we present a large scale experimental demonstration using one million phase change memory devices organized to perform a high level computational primitive by exploiting the crystallization dynamics.