Fast-Convergent Learning-aided Control in Energy Harvesting Networks

In this paper, we present a novel learning-aided energy management scheme ($\mathtt{LEM}$) for multihop energy harvesting networks. Different from prior works on this problem, our algorithm explicitly incorporates information learning into system control via a step called \emph{perturbed dual learning}. $\mathtt{LEM}$ does not require any statistical information of the system dynamics for implementation, and efficiently resolves the challenging energy outage problem. We show that $\mathtt{LEM}$ achieves the near-optimal $[O(\epsilon), O(\log(1/\epsilon)^2)]$ utility-delay tradeoff with an $O(1/\epsilon^{1-c/2})$ energy buffers ($c\in(0,1)$). More interestingly, $\mathtt{LEM}$ possesses a \emph{convergence time} of $O(1/\epsilon^{1-c/2} +1/\epsilon^c)$, which is much faster than the $\Theta(1/\epsilon)$ time of pure queue-based techniques or the $\Theta(1/\epsilon^2)$ time of approaches that rely purely on learning the system statistics. This fast convergence property makes $\mathtt{LEM}$ more adaptive and efficient in resource allocation in dynamic environments. The design and analysis of $\mathtt{LEM}$ demonstrate how system control algorithms can be augmented by learning and what the benefits are. The methodology and algorithm can also be applied to similar problems, e.g., processing networks, where nodes require nonzero amount of contents to support their actions

HMC-Based Accelerator Design For Compressed Deep Neural Networks

Deep Neural Networks (DNNs) offer remarkable performance of classifications and regressions in many high dimensional problems and have been widely utilized in real-word cognitive applications. In DNN applications, high computational cost of DNNs greatly hinder their deployment in resource-constrained applications, real-time systems and edge computing platforms. Moreover, energy consumption and performance cost of moving data between memory hierarchy and computational units are higher than that of the computation itself. To overcome the memory bottleneck, data locality and temporal data reuse are improved in accelerator design. In an attempt to further improve data locality, memory manufacturers have invented 3D-stacked memory where multiple layers of memory arrays are stacked on top of each other. Inherited from the concept of Process-In-Memory (PIM), some 3D-stacked memory architectures also include a logic layer that can integrate general-purpose computational logic directly within main memory to take advantages of high internal bandwidth during computation. In this dissertation, we are going to investigate hardware/software co-design for neural network accelerator. Specifically, we introduce a two-phase filter pruning framework for model compression and an accelerator tailored for efficient DNN execution on HMC, which can dynamically offload the primitives and functions to PIM logic layer through a latency-aware scheduling controller. In our compression framework, we formulate filter pruning process as an optimization problem and propose a filter selection criterion measured by conditional entropy. The key idea of our proposed approach is to establish a quantitative connection between filters and model accuracy. We define the connection as conditional entropy over filters in a convolutional layer, i.e., distribution of entropy conditioned on network loss. Based on the definition, different pruning efficiencies of global and layer-wise pruning strategies are compared, and two-phase pruning method is proposed. The proposed pruning method can achieve a reduction of 88% filters and 46% inference time reduction on VGG16 within 2% accuracy degradation. In this dissertation, we are going to investigate hardware/software co-design for neural network accelerator. Specifically, we introduce a two-phase filter pruning framework for model compres- sion and an accelerator tailored for efficient DNN execution on HMC, which can dynamically offload the primitives and functions to PIM logic layer through a latency-aware scheduling con- troller. In our compression framework, we formulate filter pruning process as an optimization problem and propose a filter selection criterion measured by conditional entropy. The key idea of our proposed approach is to establish a quantitative connection between filters and model accuracy. We define the connection as conditional entropy over filters in a convolutional layer, i.e., distribution of entropy conditioned on network loss. Based on the definition, different pruning efficiencies of global and layer-wise pruning strategies are compared, and two-phase pruning method is proposed. The proposed pruning method can achieve a reduction of 88% filters and 46% inference time reduction on VGG16 within 2% accuracy degradation

Phonon nanocapacitor for storage and lasing of terahertz lattice waves

We introduce a novel ultra-compact nanocapacitor of coherent phonons formed by high-finesse interference mirrors based on atomic-scale semiconductor metamaterials. Our molecular dynamics simulations show that the nanocapacitor stores THz monochromatic lattice waves, which can be used for phonon lasing - the emission of coherent phonons. Either one- or two-color phonon lasing can be realized depending on the geometry of the nanodevice. The two color regimes of the capacitor originates from the distinct transmittance dependance on the phonon wave packet incident angle for the two phonon polarizations at their respective resonances. Phonon nanocapacitor can be charged by cooling the sample equilibrated at room temperature or by the pump-probe technique. The nanocapacitor can be discharged by applying tunable reversible strain, resulting in the emission of coherent THz acoustic beams.Comment: 12 pages, 5 figure

Anonymous subject identification and privacy information management in video surveillance

The widespread deployment of surveillance cameras has raised serious privacy concerns, and many privacy-enhancing schemes have been recently proposed to automatically redact images of selected individuals in the surveillance video for protection. Of equal importance are the privacy and efficiency of techniques to first, identify those individuals for privacy protection and second, provide access to original surveillance video contents for security analysis. In this paper, we propose an anonymous subject identification and privacy data management system to be used in privacy-aware video surveillance. The anonymous subject identification system uses iris patterns to identify individuals for privacy protection. Anonymity of the iris-matching process is guaranteed through the use of a garbled-circuit (GC)-based iris matching protocol. A novel GC complexity reduction scheme is proposed by simplifying the iris masking process in the protocol. A user-centric privacy information management system is also proposed that allows subjects to anonymously access their privacy information via their iris patterns. The system is composed of two encrypted-domain protocols: The privacy information encryption protocol encrypts the original video records using the iris pattern acquired during the subject identification phase; the privacy information retrieval protocol allows the video records to be anonymously retrieved through a GC-based iris pattern matching process. Experimental results on a public iris biometric database demonstrate the validity of our framework

Web Service Deployment for Selecting a Right Steganography Scheme for Optimizing Both the Capacity and the Detectable Distortion

The principal objective of this effort is to organize a network facility to hide the secret information in an image folder without disturbing its originality. In the literature lot of algorithms are there to hide the information in an image file but most of it consumes high resource for completing the task which is not suitable for light weight mobile devices. Few basic algorithms like 1LSB, 2LSB and 3LSB methods in the literature are suitable for mobile devices since the computational complexity is very low. But, these methods either lack in maintaining the originality of the source image or in increasing the number of bits to be fixed. Furthermore, every algorithm in the literature has its own merits and demerits and we cannot predict which algorithm is best or worst since, based on the parameters such as size of the safety duplicate and encryption algorithm used to generate the cipher text the steganography schemes may produce best or worst result with respect to computational complexity, capacity, and detectable distortion. In our proposed work, we have developed a web service that takes cover image and plain text as the input from the clients and returns the steganoimage to the clients. The steganoimage will be generated by our proposed work by analyzing the above said parameters and by applying the right steganography scheme. The proposed work helps in reducing the detectable distortion, computational complexity of the client device, and in increasing the capacity. The experimental result says that, the proposed system performs better than the legacy schemes with respect to capacity, computational complexity, and detectable distortion. This proposed work is more useful to the client devices with very low computational resource since all the computational tasks are deployed in the server side

Polymer Membranes for Gas Separation

This Special Issue on “Polymer Membranes for Gas Separation” of the journal Membranes aims to offer an overview about the different applications and strategies available to improve the separation performances based on the material choice and the process conditions.Various topics have been discussed, including the synthesis and characterization of novel membrane materials, membrane aging, and the impact of process conditions on transport phenomena

Large-scale oxygen-enriched air (OEA) production from polymeric membranes for partial oxycombustion processes

Partial oxycombustion using Oxygen-Enriched Air (OEA), produced by air-gas separation with polymeric membranes, combined synergistically with CO2 capture technologies, reduced the overall energy cost of CO2 capture, and it is an exciting alternative to conventional CO2 capture technologies. An exhaustive review of polymeric membranes for this application is presented, where the best membranes showed permeability values in the range of 500-25,100 barrer and selectivities higher than 3.6. These membranes can produce OEA with oxygen molar concentrations of up to 45% for the retrofitting of large-scale power plants (~500 MWe) with partial oxycombustion. For OEA production, the polymeric membrane system is more efficient than the cryogenic distillation as the specific power consumption of the former is 43.96 kWh/ton OEA, while that of the latter is 49.57 kWh/ton OEA. This work proposes that the OEA produced by membranes feeds a partial oxy-combustion process integrated with calcium looping within a hybrid CO2 capture system. The energy consumption of the hybrid CO2 capture system proposed here is 6% lower than in the case in which OEA is produced from cryogenic distillation, which justifies the potential interest of using polymeric membranes for OEA production.Este ítem es la versión preprint del artículo. Se puede consultar la versión final aquí https://doi.org/10.1016/j.energy.2023.126697Junta de Andaluci

A survey of near-data processing architectures for neural networks

Data-intensive workloads and applications, such as machine learning (ML), are fundamentally limited by traditional computing systems based on the von-Neumann architecture. As data movement operations and energy consumption become key bottlenecks in the design of computing systems, the interest in unconventional approaches such as Near-Data Processing (NDP), machine learning, and especially neural network (NN)-based accelerators has grown significantly. Emerging memory technologies, such as ReRAM and 3D-stacked, are promising for efficiently architecting NDP-based accelerators for NN due to their capabilities to work as both high-density/low-energy storage and in/near-memory computation/search engine. In this paper, we present a survey of techniques for designing NDP architectures for NN. By classifying the techniques based on the memory technology employed, we underscore their similarities and differences. Finally, we discuss open challenges and future perspectives that need to be explored in order to improve and extend the adoption of NDP architectures for future computing platforms. This paper will be valuable for computer architects, chip designers, and researchers in the area of machine learning.This work has been supported by the CoCoUnit ERC Advanced Grant of the EU’s Horizon 2020 program (grant No 833057), the Spanish State Research Agency (MCIN/AEI) under grant PID2020-113172RB-I00, and the ICREA Academia program.Peer ReviewedPostprint (published version