E-PUR: An Energy-Efficient Processing Unit for Recurrent Neural Networks

Recurrent Neural Networks (RNNs) are a key technology for emerging applications such as automatic speech recognition, machine translation or image description. Long Short Term Memory (LSTM) networks are the most successful RNN implementation, as they can learn long term dependencies to achieve high accuracy. Unfortunately, the recurrent nature of LSTM networks significantly constrains the amount of parallelism and, hence, multicore CPUs and many-core GPUs exhibit poor efficiency for RNN inference. In this paper, we present E-PUR, an energy-efficient processing unit tailored to the requirements of LSTM computation. The main goal of E-PUR is to support large recurrent neural networks for low-power mobile devices. E-PUR provides an efficient hardware implementation of LSTM networks that is flexible to support diverse applications. One of its main novelties is a technique that we call Maximizing Weight Locality (MWL), which improves the temporal locality of the memory accesses for fetching the synaptic weights, reducing the memory requirements by a large extent. Our experimental results show that E-PUR achieves real-time performance for different LSTM networks, while reducing energy consumption by orders of magnitude with respect to general-purpose processors and GPUs, and it requires a very small chip area. Compared to a modern mobile SoC, an NVIDIA Tegra X1, E-PUR provides an average energy reduction of 92x

Online Learning Algorithm for Time Series Forecasting Suitable for Low Cost Wireless Sensor Networks Nodes

Time series forecasting is an important predictive methodology which can be applied to a wide range of problems. Particularly, forecasting the indoor temperature permits an improved utilization of the HVAC (Heating, Ventilating and Air Conditioning) systems in a home and thus a better energy efficiency. With such purpose the paper describes how to implement an Artificial Neural Network (ANN) algorithm in a low cost system-on-chip to develop an autonomous intelligent wireless sensor network. The present paper uses a Wireless Sensor Networks (WSN) to monitor and forecast the indoor temperature in a smart home, based on low resources and cost microcontroller technology as the 8051MCU. An on-line learning approach, based on Back-Propagation (BP) algorithm for ANNs, has been developed for real-time time series learning. It performs the model training with every new data that arrive to the system, without saving enormous quantities of data to create a historical database as usual, i.e., without previous knowledge. Consequently to validate the approach a simulation study through a Bayesian baseline model have been tested in order to compare with a database of a real application aiming to see the performance and accuracy. The core of the paper is a new algorithm, based on the BP one, which has been described in detail, and the challenge was how to implement a computational demanding algorithm in a simple architecture with very few hardware resources.Comment: 28 pages, Published 21 April 2015 at MDPI's journal "Sensors

Modelling of small capacity absorption chillers driven by solar thermal energy or waste heat

Aquesta recerca es centra en el desenvolupament de models en règim estacionari de màquines d’absorció de petita potència, els quals estan basats en dades altament fiables obtingudes en un banc d’assajos d’última tecnologia. Aquests models podran ser utilitzats en aplicacions de simulació, o bé per a desenvolupar estratègies de control de supervisió dels sistemes d’aire condicionat amb màquines d’absorció. Per tant, l’objectiu principal d’aquesta investigació és desenvolupar i descriure una metodologia comprensible i que englobi el procés sencer: tant els assajos, com la modelització, com també el desenvolupament d’una estratègia de control per a les màquines d’absorció de petita potència. Basant-se en la informació obtinguda de forma experimental en el banc d’assajos, s’han desenvolupat cinc models, cadascun amb una base teòrica diferent. Els resultats mostren que és possible obtenir models empírics summament precisos utilitzant únicament com a paràmetres d’entrada les variables dels circuits externs d’aigua. Aquest treball finalitza amb la proposta de dues estratègies òptimes de control i el seu ús per al control on-line de sistemes basats en refredadores tèrmiques d’absorció.This research deals with the development of the simple, yet accurate steady-state models of small capacity absorption machines which are based on highly reliable data obtained in the state-of-the-art test bench. These models can further be used in simulation tools or to develop supervisory control strategies for air-conditioning systems with absorption machines. Therefore, the main aim of this research is to develop and to describe a comprehensive methodology which encloses entire process which consists of testing, modelling and control strategy development of small capacity absorption machines. Five different models are developed based on the experimental data obtained in the test bench. The results show that it is possible to develop highly accurate empirical models by using only the variables of external water circuits as input parameters. Finally, two optimal control strategies are developed to demonstrate how these models can be used for on-line control of absorption systems

Roadmap on semiconductor-cell biointerfaces.

This roadmap outlines the role semiconductor-based materials play in understanding the complex biophysical dynamics at multiple length scales, as well as the design and implementation of next-generation electronic, optoelectronic, and mechanical devices for biointerfaces. The roadmap emphasizes the advantages of semiconductor building blocks in interfacing, monitoring, and manipulating the activity of biological components, and discusses the possibility of using active semiconductor-cell interfaces for discovering new signaling processes in the biological world

Multifunctional Optoelectronic Device Based on Resistive Switching Effects

Optoelectronic resistive switching devices, utilizing optical and electrical hybrid methods to control the resistance states, offer several advantages of both photons and electrons for high-performance information detecting, demodulating, processing, and memorizing. In the past decades, optoelectronic resistive switching devices have been widely discussed and studied due to the potential for parallel information transmission and processing. In this chapter, recent progresses on the optoelectronic resistive switching mechanism, materials, and devices will be introduced. Then, their performance such as photoresponsivity, on/off ratio, as well as retention will be investigated. Furthermore, possible applications of the optoelectronic resistive switching considering logic, memory, neuromorphic, and image-processing devices will be summarized. In the end, the challenges and possible solutions of optoelectronic resistive switching devices for the next-generation information technology will be discussed and prospected

Magnetic Cellular Nonlinear Network with Spin Wave Bus for Image Processing

We describe and analyze a cellular nonlinear network based on magnetic nanostructures for image processing. The network consists of magneto-electric cells integrated onto a common ferromagnetic film - spin wave bus. The magneto-electric cell is an artificial two-phase multiferroic structure comprising piezoelectric and ferromagnetic materials. A bit of information is assigned to the cell's magnetic polarization, which can be controlled by the applied voltage. The information exchange among the cells is via the spin waves propagating in the spin wave bus. Each cell changes its state as a combined effect of two: the magneto-electric coupling and the interaction with the spin waves. The distinct feature of the network with spin wave bus is the ability to control the inter-cell communication by an external global parameter - magnetic field. The latter makes possible to realize different image processing functions on the same template without rewiring or reconfiguration. We present the results of numerical simulations illustrating image filtering, erosion, dilation, horizontal and vertical line detection, inversion and edge detection accomplished on one template by the proper choice of the strength and direction of the external magnetic field. We also present numerical assets on the major network parameters such as cell density, power dissipation and functional throughput, and compare them with the parameters projected for other nano-architectures such as CMOL-CrossNet, Quantum Dot Cellular Automata, and Quantum Dot Image Processor. Potentially, the utilization of spin waves phenomena at the nanometer scale may provide a route to low-power consuming and functional logic circuits for special task data processing