scholarly journals On Predictable Reconfigurable System Design

2021 ◽  
Vol 18 (2) ◽  
pp. 1-28
Author(s):  
Nils Voss ◽  
Bastiaan Kwaadgras ◽  
Oskar Mencer ◽  
Wayne Luk ◽  
Georgi Gaydadjiev
Keyword(s):  
Design Methodology ◽  
Hardware Implementation ◽  
State Of The Art ◽  
Hardware Acceleration ◽  
Software Models ◽  
Reconfigurable System ◽  
Application Analysis ◽  
Analytical Estimation ◽  
First Time ◽  
Suitable System

We propose a design methodology to facilitate rigorous development of complex applications targeting reconfigurable hardware. Our methodology relies on analytical estimation of system performance and area utilisation for a given specific application and a particular system instance consisting of a controlflow machine working in conjunction with one or more reconfigurable dataflow accelerators. The targeted application is carefully analyzed, and the parts identified for hardware acceleration are reimplemented as a set of representative software models. Next, with the results of the application analysis, a suitable system architecture is devised and its performance is evaluated to determine bottlenecks, allowing predictable design. The architecture is iteratively refined, until the final version satisfying the specification requirements in terms of performance and required hardware area is obtained. We validate the presented methodology using a widely accepted convolutional neural network (VGG-16) and an important HPC application (BQCD). In both cases, our methodology relieved and alleviated all system bottlenecks before the hardware implementation was started. As a result the architectures were implemented first time right, achieving state-of-the-art performance within 15% of our modelling estimations.

scholarly journals Hardware Implementation of the CCSDS 123.0-B-2 Near-Lossless Compression Standard Following an HLS Design Methodology

2021 ◽  
Vol 13 (21) ◽  
pp. 4388
Author(s):  
Yubal Barrios ◽  
Antonio Sánchez ◽  
Raúl Guerra ◽  
Roberto Sarmiento
Keyword(s):  
System Performance ◽  
Design Methodology ◽  
Hardware Implementation ◽  
State Of The Art ◽  
Lossless Compression ◽  
High Resolution Imaging ◽  
Design Time ◽  
Resolution Imaging ◽  
Imaging Sensors ◽  
And Storage

The increment in the use of high-resolution imaging sensors on-board satellites motivates the use of on-board image compression, mainly due to restrictions in terms of both hardware (computational and storage resources) and downlink bandwidth with the ground. This work presents a compression solution based on the CCSDS 123.0-B-2 near-lossless compression standard for multi- and hyperspectral images, which deals with the high amount of data acquired by these next-generation sensors. The proposed approach has been developed following an HLS design methodology, accelerating design time and obtaining good system performance. The compressor is comprised by two main stages, a predictor and a hybrid encoder, designed in Band-Interleaved by Line (BIL) order and optimized to achieve a trade-off between throughput and logic resources utilization. This solution has been mapped on a Xilinx Kintex UltraScale XCKU040 FPGA and targeting AVIRIS images, reaching a throughput of 12.5 MSamples/s and consuming only the 7% of LUTs and around the 14% of dedicated memory blocks available in the device. To the best of our knowledge, this is the first fully-compliant hardware implementation of the CCSDS 123.0-B-2 near-lossless compression standard available in the state of the art.

UL-CNN: An Ultra-Lightweight Convolutional Neural Network Aiming at Flash-Based Computing-In-Memory Architecture for Pedestrian Recognition

2020 ◽  
pp. 2150022
Author(s):  
Chen Yang ◽  
Jingyu Zhang ◽  
Qi Chen ◽  
Yi Xu ◽  
Cimang Lu
Keyword(s):  
Neural Network ◽  
Convolutional Neural Network ◽  
Design Methodology ◽  
Hardware Implementation ◽  
State Of The Art ◽  
Memory Architecture ◽  
Storage Overhead ◽  
Speed Up ◽  
Memory Bottleneck ◽  
On Chip

Pedestrian recognition has achieved the state-of-the-art performance due to the progress of recent convolutional neural network (CNN). However, mainstream CNN models are too complicated to emerging Computing-In-Memory (CIM) architectures for hardware implementation, because enormous parameters and massive intermediate processing results may incur severe “memory bottleneck”. This paper proposed a design methodology of Parameter Substitution with Nodes Compensation (PSNC) to significantly reduce parameters of CNN model without inference accuracy degradation. Based on the PSNC methodology, an ultra-lightweight convolutional neural network (UL-CNN) was designed. The UL-CNN model is a specially optimized convolutional neural network aiming at a flash-based CIM architecture (Conv-Flash) and to apply for recognizing person. The implementation result of running UL-CNN on Conv-Flash shows that the inference accuracy is up to 94.7%. Compared to LeNet-5, on the premise of the similar operations and accuracy, the amounts of UL-CNN’s parameters are less than 37% of LeNet-5 at the same dataset benchmark. Such parameter reduction can dramatically speed up the training process and economize on-chip storage overhead, as well as save the power consumption of the memory access. With the aid of UL-CNN, the Conv-Flash architecture can provide the best energy efficiency compared to other platforms (CPU, GPU, FPGA, etc.), which consumes only 2.2[Formula: see text] 105J to complete pedestrian recognition for one frame.

scholarly journals Longitudinal Speech Biomarkers for Automated Alzheimer's Detection

2021 ◽  
Vol 3 ◽  
Author(s):  
Jordi Laguarta ◽  
Brian Subirana
Keyword(s):  
Design Methodology ◽  
State Of The Art ◽  
Saliency Map ◽  
Cultural Bias ◽  
Detection Accuracy ◽  
Audio Processing ◽  
Target Disease ◽  
Multiple Biomarkers ◽  
Multimodal Biomarkers ◽  
First Time

We introduce a novel audio processing architecture, the Open Voice Brain Model (OVBM), improving detection accuracy for Alzheimer's (AD) longitudinal discrimination from spontaneous speech. We also outline the OVBM design methodology leading us to such architecture, which in general can incorporate multimodal biomarkers and target simultaneously several diseases and other AI tasks. Key in our methodology is the use of multiple biomarkers complementing each other, and when two of them uniquely identify different subjects in a target disease we say they are orthogonal. We illustrate the OBVM design methodology by introducing sixteen biomarkers, three of which are orthogonal, demonstrating simultaneous above state-of-the-art discrimination for two apparently unrelated diseases such as AD and COVID-19. Depending on the context, throughout the paper we use OVBM indistinctly to refer to the specific architecture or to the broader design methodology. Inspired by research conducted at the MIT Center for Brain Minds and Machines (CBMM), OVBM combines biomarker implementations of the four modules of intelligence: The brain OS chunks and overlaps audio samples and aggregates biomarker features from the sensory stream and cognitive core creating a multi-modal graph neural network of symbolic compositional models for the target task. In this paper we apply the OVBM design methodology to the automated diagnostic of Alzheimer's Dementia (AD) patients, achieving above state-of-the-art accuracy of 93.8% using only raw audio, while extracting a personalized subject saliency map designed to longitudinally track relative disease progression using multiple biomarkers, 16 in the reported AD task. The ultimate aim is to help medical practice by detecting onset and treatment impact so that intervention options can be longitudinally tested. Using the OBVM design methodology, we introduce a novel lung and respiratory tract biomarker created using 200,000+ cough samples to pre-train a model discriminating cough cultural origin. Transfer Learning is subsequently used to incorporate features from this model into various other biomarker-based OVBM architectures. This biomarker yields consistent improvements in AD detection in all the starting OBVM biomarker architecture combinations we tried. This cough dataset sets a new benchmark as the largest audio health dataset with 30,000+ subjects participating in April 2020, demonstrating for the first time cough cultural bias.

Interest rate option hedging portfolios without bank account

2019 ◽  
Vol 37 (1) ◽  
pp. 134-142
Author(s):  
Alberto Bueno-Guerrero
Keyword(s):  
Design Methodology ◽  
Contingent Claim ◽  
Necessary And Sufficient Condition ◽  
Barrier Option ◽  
Bank Account ◽  
Content Type ◽  
Option Hedging ◽  
Hedging Portfolio ◽  
Necessary And Sufficient ◽  
First Time

Purpose This paper aims to study the conditions for the hedging portfolio of any contingent claim on bonds to have no bank account part. Design/methodology/approach Hedging and Malliavin calculus techniques recently developed under a stochastic string framework are applied. Findings A necessary and sufficient condition for the hedging portfolio to have no bank account part is found. This condition is applied to a barrier option, and an example of a contingent claim whose hedging portfolio has a bank account part different from zero is provided. Originality/value To the best of the authors’ knowledge, this is the first time that this issue has been addressed in the literature.

scholarly journals State-of-the-Art Char Production with a Focus on Bark Feedstocks: Processes, Design, and Applications

Processes ◽  
2021 ◽  
Vol 9 (1) ◽  
pp. 87
Author(s):  
Ali Umut Şen ◽  
Helena Pereira
Keyword(s):  
State Of The Art ◽  
Lignocellulosic Materials ◽  
Heating Rates ◽  
Production Methods ◽  
Slow Pyrolysis ◽  
Production Studies ◽  
Lignocellulosic Feedstocks ◽  
Thermochemical Method ◽  
Superior Surface ◽  
First Time

In recent years, there has been a surge of interest in char production from lignocellulosic biomass due to the fact of char’s interesting technological properties. Global char production in 2019 reached 53.6 million tons. Barks are among the most important and understudied lignocellulosic feedstocks that have a large potential for exploitation, given bark global production which is estimated to be as high as 400 million cubic meters per year. Chars can be produced from barks; however, in order to obtain the desired char yields and for simulation of the pyrolysis process, it is important to understand the differences between barks and woods and other lignocellulosic materials in addition to selecting a proper thermochemical method for bark-based char production. In this state-of-the-art review, after analyzing the main char production methods, barks were characterized for their chemical composition and compared with other important lignocellulosic materials. Following these steps, previous bark-based char production studies were analyzed, and different barks and process types were evaluated for the first time to guide future char production process designs based on bark feedstock. The dry and wet pyrolysis and gasification results of barks revealed that application of different particle sizes, heating rates, and solid residence times resulted in highly variable char yields between the temperature range of 220 °C and 600 °C. Bark-based char production should be primarily performed via a slow pyrolysis route, considering the superior surface properties of slow pyrolysis chars.

scholarly journals UV Inscription and Pressure Induced Long-Period Gratings through 3D Printed Amplitude Masks

Sensors ◽  
2021 ◽  
Vol 21 (6) ◽  
pp. 1977
Author(s):  
Ricardo Oliveira ◽  
Liliana M. Sousa ◽  
Ana M. Rocha ◽  
Rogério Nogueira ◽  
Lúcia Bilro
Keyword(s):  
State Of The Art ◽  
Low Cost ◽  
Resonance Wavelength ◽  
Complex Structures ◽  
Pressing Method ◽  
Long Period ◽  
Long Period Gratings ◽  
3D Printed ◽  
Mechanical Pressing ◽  
First Time

In this work, we demonstrate for the first time the capability to inscribe long-period gratings (LPGs) with UV radiation using simple and low cost amplitude masks fabricated with a consumer grade 3D printer. The spectrum obtained for a grating with 690 µm period and 38 mm length presented good quality, showing sharp resonances (i.e., 3 dB bandwidth < 3 nm), low out-of-band loss (~0.2 dB), and dip losses up to 18 dB. Furthermore, the capability to select the resonance wavelength has been demonstrated using different amplitude mask periods. The customization of the masks makes it possible to fabricate gratings with complex structures. Additionally, the simplicity in 3D printing an amplitude mask solves the problem of the lack of amplitude masks on the market and avoids the use of high resolution motorized stages, as is the case of the point-by-point technique. Finally, the 3D printed masks were also used to induce LPGs using the mechanical pressing method. Due to the better resolution of these masks compared to ones described on the state of the art, we were able to induce gratings with higher quality, such as low out-of-band loss (0.6 dB), reduced spectral ripples, and narrow bandwidths (~3 nm).

scholarly journals Electro-Quasistatic Animal Body Communication for Untethered Rodent Biopotential Recording

2021 ◽  
Vol 11 (1) ◽  
Author(s):  
Shreeya Sriram ◽  
Shitij Avlani ◽  
Matthew P. Ward ◽  
Shreyas Sen
Keyword(s):  
Lower Energy ◽  
Sensor Node ◽  
State Of The Art ◽  
The Body ◽  
Animal Body ◽  
Current State ◽  
Communication Modalities ◽  
First Time ◽  
Biopotential Recording

AbstractContinuous multi-channel monitoring of biopotential signals is vital in understanding the body as a whole, facilitating accurate models and predictions in neural research. The current state of the art in wireless technologies for untethered biopotential recordings rely on radiative electromagnetic (EM) fields. In such transmissions, only a small fraction of this energy is received since the EM fields are widely radiated resulting in lossy inefficient systems. Using the body as a communication medium (similar to a ’wire’) allows for the containment of the energy within the body, yielding order(s) of magnitude lower energy than radiative EM communication. In this work, we introduce Animal Body Communication (ABC), which utilizes the concept of using the body as a medium into the domain of untethered animal biopotential recording. This work, for the first time, develops the theory and models for animal body communication circuitry and channel loss. Using this theoretical model, a sub-inch$$^3$$ 3 [1″ × 1″ × 0.4″], custom-designed sensor node is built using off the shelf components which is capable of sensing and transmitting biopotential signals, through the body of the rat at significantly lower powers compared to traditional wireless transmissions. In-vivo experimental analysis proves that ABC successfully transmits acquired electrocardiogram (EKG) signals through the body with correlation $$>99\%$$ > 99 % when compared to traditional wireless communication modalities, with a 50$$\times$$ × reduction in power consumption.

scholarly journals Thermoelectric Sensor Coupled Yagi–Uda Nanoantenna for Infrared Detection

Electronics ◽  
2021 ◽  
Vol 10 (5) ◽  
pp. 527
Author(s):  
Waleed Tariq Sethi ◽  
Olivier De Sagazan ◽  
Mohamed Himdi ◽  
Hamsakutty Vettikalladi ◽  
Saleh A. Alshebeili
Keyword(s):  
State Of The Art ◽  
Infrared Detection ◽  
Single Element ◽  
Center Wavelength ◽  
Resonance Response ◽  
Thermal Measurements ◽  
Radiation Induced ◽  
Thermoelectric Sensor ◽  
Junction Array ◽  
First Time

We present an experimental demonstration of a thermoelectric sensor coupled with a nanoantenna as an alternative option for detecting infrared energy. Two nanoantenna design (single element and an array) variations based on Yagi-Uda technology and one separate nano-thermoelectric junction array were fabricated and tested. The nanoantennas were tuned to operate and respond at a center wavelength of 1550 nm (193.5 THz) optical C-band window, but they also exhibited a resonance response when excited by lasers of various wavelengths (650 nm and 940 nm). The radiation-induced electric currents in the nanoantennas, coupled with a nano-thermoelectric sensor, produced a potential difference as per the Seebeck effect. With respect to the uniform thermal measurements of the reference nanoantenna, the experiments confirmed the detection properties of the proposed nanoantennas; the single element detected a peak percentage voltage hike of 28%, whereas the array detected a peak percentage voltage hike of 80% at the center wavelength. Compared to state-of-the-art thermoelectric designs, this was the first time that such peak percentage voltages were experimentally reported following a planar design based on the Seebeck principle.

scholarly journals Domain Adaptation Using a Three-Way Decision Improves the Identification of Autism Patients from Multisite fMRI Data

2021 ◽  
Vol 11 (5) ◽  
pp. 603
Author(s):  
Chunlei Shi ◽  
Xianwei Xin ◽  
Jiacai Zhang
Keyword(s):  
Machine Learning ◽  
Domain Adaptation ◽  
Recognition Accuracy ◽  
State Of The Art ◽  
Autism Spectrum ◽  
Fmri Data ◽  
Target Domain ◽  
Sample Distribution ◽  
Machine Learning Methods ◽  
First Time

Machine learning methods are widely used in autism spectrum disorder (ASD) diagnosis. Due to the lack of labelled ASD data, multisite data are often pooled together to expand the sample size. However, the heterogeneity that exists among different sites leads to the degeneration of machine learning models. Herein, the three-way decision theory was introduced into unsupervised domain adaptation in the first time, and applied to optimize the pseudolabel of the target domain/site from functional magnetic resonance imaging (fMRI) features related to ASD patients. The experimental results using multisite fMRI data show that our method not only narrows the gap of the sample distribution among domains but is also superior to the state-of-the-art domain adaptation methods in ASD recognition. Specifically, the ASD recognition accuracy of the proposed method is improved on all the six tasks, by 70.80%, 75.41%, 69.91%, 72.13%, 71.01% and 68.85%, respectively, compared with the existing methods.

A hierarchical oxygen vacancy-rich WO3 with “nanowire-array-on-nanosheet-array” structure for highly efficient oxygen evolution reaction

2019 ◽  
Vol 7 (12) ◽  
pp. 6730-6739 ◽  
Author(s):  
Jinxiang Diao ◽  
Wenyu Yuan ◽  
Yu Qiu ◽  
Laifei Cheng ◽  
Xiaohui Guo
Keyword(s):  
Oxygen Vacancy ◽  
Oxygen Evolution Reaction ◽  
Oxygen Vacancies ◽  
State Of The Art ◽  
Nanowire Array ◽  
Nanowire Arrays ◽  
Nanosheet Array ◽  
Nanosheet Arrays ◽  
Array Structure ◽  
First Time

Hierarchical vertical WO3 nanowire arrays on vertical WO3 nanosheet arrays with rich oxygen vacancies were synthesized via a simple and facile method, and the outstanding OER performance which is superior to that of most reported state-of-the-art catalysts was reported for the first time.

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