Co-exploration of neural architectures and hardware design is promising due to its capability simultaneously optimize network accuracy efficiency. However, state-of-the-art architecture search algorithms for the co-exploration are dedicated conventional von-Neumann computing architecture, whose performance heavily limited by well-known memory wall. In this article, we first bring computing-in-m...

Neuromorphic Computing, a concept pioneered in the late 1980s, is receiving lot of attention lately due to its promise reducing computational energy, latency, as well learning complexity artificial neural networks. Taking inspiration from neuroscience, this interdisciplinary field performs multi-stack optimization across devices, circuits, and algorithms by providing an end-to-end approach achi...

Abstract This paper proposes the use of astrocytes to realize Boolean logic gates, through manipulation threshold $$\hbox {Ca}^{2+}$$ Ca 2 + ion flows between cells based on input signals. Through wet-lab experiments that en...

This paper presents a short introduction to the Reservoir Computing and Extreme Learning Machine main ideas and developments. While both methods make use of Neural Networks and Random Projections, Reservoir Computing allows the network to have a recurrent structure, while the Extreme Learning Machine is a Feedforward neural network only. Some state of the art techniques are briefly presented an...

Spiking neural P systems with astrocytes (SNPA systems, for short) are a class of distributed parallel computing devices inspired from the way neurons communicate by means of spikes, where also astrocytes are considered, having an excitatory or an inhibitory influence on synapses. Looking for small universal computing devices is a classical research topic in computer science. In this work, we i...

Frequency information processing using complex valued neural networks is proposed Learning process is realized by adjusting delay time and conductance of neural connections Experimental results demonstrate that the network learns successfully an intended output pro le smoothly in frequency domain This result is applicable not only to frequency signal processing but also to future optical neural...

The economic consequence of corporate failure is enormous, especially for the stakeholders of public-held companies. Prior to a corporate failure, the firm’s financial status is frequently in distress. Consequently, finding a method to identify corporate financial distress as early as possible is clearly a matter of considerable interest to investors, creditors, auditors and other stakeholders....

Pervasive computing is often mentioned in the context of improving healthcare. This paper presents a novel approach for diagnosing diabetes using neural networks and pervasive healthcare computing technologies. The recent developments in small mobile devices and wireless communications provide a strong motivation to develop new software techniques and mobile services for pervasive healthcare co...