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We employed the dynamic memory architecture to achieve real-time information exchange.

It provides guidance to the embedded system designers to tailor the memory architecture to their needs.

In this context, a biomimetic episodic memory architecture to encode diverse learning experiences of iCub humanoid is presented.

The method used there, developed in the 1990's, is based on algorithms that use a massively parallel associative memory architecture to identify patterns efficiently at high speed.

In this paper, we propose a low-complexity QPP interleaving address generator and a multi-bank memory architecture to enable parallel Turbo decoding.

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To implement this, the current memory architecture has to be hierarchically structured by adding further edge types.

Cloud computing instances were initialized using memory-optimized architecture to memory requirements the high memory requirements of Velvet-Oases assembly of 454 sequence reads.

While the increasing sensor accuracy and the speed of signal processing failed to bridge the gap between the performance of artificial and human sensorimotor systems, the motor memory architecture seems to remain neglected.

We have modified our memory architecture in order to adapt it to our SFQ microprocessor, CORE1.

In [36], Graves has proposed to apply a long short-term memory (LSTM) architecture to speech recognition to provide a more robust and biologically alternative to statistical learning methods such as HMMs.

To estimate this number for our memory architecture we need to determine the number of possible combinations of property nodes.

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