A Noniterative Supervised On-Chip Training Circuitry for Reservoir Computing Systems.

Artificial neural networks (ANNs) is an exponentially growing field, mainly because of its wide range of applications to everyday life such as pattern recognition or time series forecasting. In particular, reservoir computing (RC) arises as an optimal computational framework suited for temporal/sequential data analysis. The direct on-silicon implementation of RCs may help to minimize power and maximize processing speed, that is especially relevant in edge intelligence applications where energy storage is considerably restricted.

Fully Parallel Stochastic Computing Hardware Implementation of Convolutional Neural Networks for Edge Computing Applications.

Edge artificial intelligence (AI) is receiving a tremendous amount of interest from the machine learning community due to the ever-increasing popularization of the Internet of Things (IoT). Unfortunately, the incorporation of AI characteristics to edge computing devices presents the drawbacks of being power and area hungry for typical deep learning techniques such as convolutional neural networks (CNNs). In this work, we propose a power-and-area efficient architecture based on the exploitation of the correlation phenomenon in stochastic computing (SC) systems.

Compact Hardware Synthesis of Stochastic Spiking Neural Networks.

Spiking neural networks (SNN) are able to emulate real neural behavior with high confidence due to their bio-inspired nature. Many designs have been proposed for the implementation of SNN in hardware, although the realization of high-density and biologically-inspired SNN is currently a complex challenge of high scientific and technical interest. In this work, we propose a compact digital design for the implementation of high-volume SNN that considers the intrinsic stochastic processes present in biological neurons and enables high-density hardware implementation.

An improved methodology to compute surface site interaction points using high density molecular electrostatic potential surfaces.

The theoretical calculation of Surface Site Interaction Points (SSIP) has been used successfully in some applications in the solid and liquid phase. In this work we propose a new set of optimizations for the search of SSIP using the Molecular Electrostatic Potential Surfaces (MEPS) calculated with Density Functional Theory and B3LYP/6-31*G basis set. The measures that have been implemented are based on the search for the best agreement between experimental H-bond donor and acceptor parameters (α and β) and the MEPS extremes exploring a range of electron density levels.

A Bayesian Target Predictor Method based on Molecular Pairing Energies estimation.

Virtual screening (VS) is applied in the early drug discovery phases for the quick inspection of huge molecular databases to identify those compounds that most likely bind to a given drug target. In this context, there is the necessity of the use of compact molecular models for database screening and precise target prediction in reasonable times. In this work we present a new compact energy-based model that is tested for its application to Virtual Screening and target prediction.

A surface site interaction point methodology for macromolecules and huge molecular databases.

Determining the position and magnitude of Surface Site Interaction Points (SSIP) is a useful technique for understanding intermolecular interactions. SSIPs have been used for the prediction of solvation properties and for virtual co-crystal screening. To determine the SSIPs for a molecule, the Molecular Electrostatic Potential Surface (MEPS) is first calculated using ab initio methods such as Density Functional Theory.

High-Density Liquid-State Machine Circuitry for Time-Series Forecasting.

Spiking neural networks (SNN) are the last neural network generation that try to mimic the real behavior of biological neurons. Although most research in this area is done through software applications, it is in hardware implementations in which the intrinsic parallelism of these computing systems are more efficiently exploited. Liquid state machines (LSM) have arisen as a strategic technique to implement recurrent designs of SNN with a simple learning methodology.

FPGA-Based Stochastic Echo State Networks for Time-Series Forecasting.

Hardware implementation of artificial neural networks (ANNs) allows exploiting the inherent parallelism of these systems. Nevertheless, they require a large amount of resources in terms of area and power dissipation. Recently, Reservoir Computing (RC) has arisen as a strategic technique to design recurrent neural networks (RNNs) with simple learning capabilities.

Ultra-fast data-mining hardware architecture based on stochastic computing.

Minimal hardware implementations able to cope with the processing of large amounts of data in reasonable times are highly desired in our information-driven society. In this work we review the application of stochastic computing to probabilistic-based pattern-recognition analysis of huge database sets. The proposed technique consists in the hardware implementation of a parallel architecture implementing a similarity search of data with respect to different pre-stored categories.

A New Stochastic Computing Methodology for Efficient Neural Network Implementation.

This paper presents a new methodology for the hardware implementation of neural networks (NNs) based on probabilistic laws. The proposed encoding scheme circumvents the limitations of classical stochastic computing (based on unipolar or bipolar encoding) extending the representation range to any real number using the ratio of two bipolar-encoded pulsed signals. Furthermore, the novel approach presents practically a total noise-immunity capability due to its specific codification.

Studying the role of synchronized and chaotic spiking neural ensembles in neural information processing.

The brain is characterized by performing many diverse processing tasks ranging from elaborate processes such as pattern recognition, memory or decision making to more simple functionalities such as linear filtering in image processing. Understanding the mechanisms by which the brain is able to produce such a different range of cortical operations remains a fundamental problem in neuroscience. Here we show a study about which processes are related to chaotic and synchronized states based on the study of in-silico implementation of Stochastic Spiking Neural Networks (SSNN).

Hardware implementation of stochastic spiking neural networks.

Spiking Neural Networks, the last generation of Artificial Neural Networks, are characterized by its bio-inspired nature and by a higher computational capacity with respect to other neural models. In real biological neurons, stochastic processes represent an important mechanism of neural behavior and are responsible of its special arithmetic capabilities. In this work we present a simple hardware implementation of spiking neurons that considers this probabilistic nature.

Chaos-based mixed signal implementation of spiking neurons.

A new design of Spiking Neural Networks is proposed and fabricated using a 0.35 microm CMOS technology. The architecture is based on the use of both digital and analog circuitry.