Multi-Tasking Memcapacitive Networks

Recent studies have shown that networks of memcapacitive devices provide an ideal computing platform of low power consumption for reservoir computing systems. Random, crossbar, or small-world power-law (SWPL) structures are common topologies for reservoir substrates to compute single tasks. However, neurological studies have shown that the interconnections of cortical brain regions associated with different functions form a rich-club structure. This structure allows human brains to perform multiple activities simultaneously. So far, memcapacitive reservoirs can perform only single tasks. Here, we propose, for the first time, cluster networks functioning as memcapacitive reservoirs to perform multiple tasks simultaneously. Our results illustrate that cluster networks surpassed crossbar and SWPL networks by factors of
$4.1\times, 5.2\times $
, and
$1.7\times $
on three tasks: Isolated Spoken Digits, MNIST, and CIFAR-10. Compared to single-task networks in our previous and published results, multitasking cluster networks could accomplish similar accuracies of 86%, 94.4%, and 27.9% for MNIST, Isolated Spoken Digits, and CIFAR-10. Our extended simulations reveal that both the input signal amplitudes and the inter-cluster connections contribute to the accuracy of cluster networks. Selecting optimal values for signal amplitudes and inter-cluster links is key to obtaining high classification accuracy and low power consumption. Our results illustrate the promise of memcapacitive brain-inspired cluster networks and their capability to solve multiple tasks simultaneously. Such novel computing architectures have the potential to make edge applications more efficient and allow systems that cannot be reconfigured to solve multiple tasks.