Multi-Tasking Memcapacitive Networks

Memcapacitive neural networks

We show that memcapacitive (memory capacitive) systems can be used as synapses in artificial neural networks. As an example of our approach, we discuss the architecture of an integrate-and-fire neural network based on memcapacitive synapses. Moreover, we demonstrate that the spike-timing-dependent plasticity can be simply realized with some of these devices. Memcapacitive synapses are a low-ene...

Multi-tasking for TinyOS

Tasks in TinyOS execute non-preemptively and run to completion, forcing programmers to keep individual tasks short and spread lengthy operations across multiple tasks – a major divergence from conventional programming paradigms. This report documents our attempt to incorporate multi-tasking into TinyOS and its subsequent effect on power consumption of “motes”. We succeeded in modifying the Tiny...

Interconnection Networks Enable Fine-Grain Dynamic Multi-tasking on FPGAs

Multimedia support appears on embedded platforms, such as WAP for mobile phones. However, true multimedia applications require both the computation power that only dedicated hardware can provide and the flexibility of software implementations. To this end, we are investigating reconfigurable architectures, composed of an instruction-set processor running software processes and coupled to an FPG...

Immune networks: multi-tasking capabilities at medium load

Associative network models featuring multi-tasking properties have been introduced recently and studied in the low load regime, where the number P of simultaneously retrievable patterns scales with the number N of nodes as P ∼ logN . In addition to their relevance in artificial intelligence, these models are increasingly important in immunology, where stored patterns represent strategies to fig...

Contests with multi-tasking