Other: General Processor
General Purpose Neural Processing Unit
AP031 »
[The General Purpose Neural Processor Unit proposed in this project aims to simulate and cluster an SNN-based neural network that can solve at least one problem.]
There is a saying, butterfly effect. This means that the small wings of butterflies drive a big typhoon. Likewise, something happens under the influence of many things. For example, solar power generation is affected by wind, cloud, temperature, etc., and farming is affected by solar radiation, precipitation, and temperature. To solve these nonlinear problems, methods such as machine learning have emerged these days.
Machine learning is an algorithm that uses neural networks to analyze data and make decisions based on learned information. Since various types of data can be used as data for learning, it is suitable and widely used to solve nonlinear problems. However, it has only recently begun to be used because it requires a lot of computing power. And even now, big models take a lot of time.
As a way to solve this problem, a neural network model was designed and uploaded to the FPGA for use. But one neural network model had the disadvantage of being able to solve only one problem. To this end, accelerators such as NPUs equipped with many modules that perform repeated operations (mainly convolution operations) also came out.
These NPUs were created with neural networks such as CNN and RNN, which are second-generation neural networks. However, research on SNN, a third-generation neural network, is active these days, and NPU using it is being designed. A typical example is IBM's Truenorth. SNN is a neural network that mimics real neurons and has several advantages in terms of power and learning. In addition, SNN is completely bio-plausible, so it is an essential route in the future to implement artificial intelligence.
Several neurons gather to create a neural network system, and the system gathers to form a neural network network. As things happen under the influence of many things, data on many things are important. Each task is proposed as an idea to break away from not the basic way provided to the neural network as a variable and but solve it through network connection and cluster.
Other: AI
Jaguar
AS023 »
"Softbank expects ARM to deliver 1 trillion IoT chips in the next 20 years." (Reuters, 2017)
Since AI is considered as an emerging technology by many companies and research institutes in the world because of its performance and accuracy, the number of AI chips and relevant systems are increased exponentially as Softbank already expected in 2017. With this kind of technical evaluation, it is expected that AI systems will help people to make their life richer than in the past because of its versatile functions and better performance than human-being.
However, people are also faced with side effects of AI systems' exponential increment because recently many researchers found that AI inference and training sequences can generate numerous CO2s. For example, training a single AI model can generate 626,000 pounds of carbon dioxide from relevant operations. This amount is five times bigger than a car. (MIT Technology Review, 2019)
For that reason, TinyML with lower-power and high-performance features is emerging as a replacement of ultra-scale AI systems.
To contribute to maintaining sustainability for the next generation, we will focus on designing low-power and high-performance TinyML accelerator with minimal hardware resources and energy consumption. To realize this goal, we will use Intel FPGA-based IoT device kits, self-developed hardware architecture and optimized software stacks for covering AI full stacks and contributing to CO2 reduction with low-power features for global sustainability.
Smart City
Coprocessor-based Antivirus in Order to Detect Malware Preventively
AS025 »
Distinct metamodels of Smart Cities have been developed in order to solve the problems of cities in relation to different socioeconomic indicators. In view of the foregoing, information security plays an important role in guaranteeing human rights in contemporary society. From an infection by malware (malicious + software), a person or institution can suffer irrecoverable losses. As for a person, bank passwords, social networks, intimate photos or videos can be shared across the world wide web, which will affect finances, dignity and mental health. On the other side, an institution may have its vital data inaccessible and/or information from its respective customers and employees stolen. In synthesis, the theft of intimate information can lead to cases of depression, suicide and other mental disorders.
The proposed work investigates 86 commercial antiviruses. About 17% of the antiviruses did not recognize the existence of the malicious samples analyzed. Commercial antiviruses, which, even with billionaire revenue, have low effectiveness and have been criticized by incident researchers for more than a decade. Commercial antiviruses performance is based on signatures when the suspect executable is compared to a blacklist made from previous reports (and this requires that there have already had victims). Blacklists are assumed to be effectively null by the current worldwide rate of creation of virtual pests, that is 8 (eight) new malwares per second. We concluded that malwares have the ability to deceive antiviruses and other cyber-surveillance mechanisms
In order to overcome the limitations of commercial antiviruses, this project creates a core processor-based antivirus able to identify the modus operandi of a malware application before it is even executed by the user. So, our goal is to propose an antivirus, endowed with artificial intelligence, able to identify malwares through models based on fast training and high-performance neural networks. Our core processor-based antivirus is equipped with an authorial Extreme Learning Machine.
Our processor achieves an average accuracy of 99.80% in the discrimination between benign and malware executables. Preliminary results indicate that the authorial Coprocessor, built on FPGA, can speed up the response time of the proposed antivirus by about 4765 times compared to the CPU implementation employing the same FPGA. Thus, the malicious intent of the malware is preemptively detected even when executed on a slow (low processing power) device. Our antivirus enables high performance, large capacity of parallelism, and simple, low-power architecture with low power consumption. We concluded that our solution assists the main requirements for the proper operation and confection of an antivirus in hardware.
Smart City
桥梁裂缝动态监测系统
PR019 »
桥梁作为当今重要的交通枢纽，与行车的安全与畅通息息相关，它的正常运行能极大地推动经济的发展，促进社会的进步。随着时间的推移，外力的反复碾压及雨雪、洪水、冰冻、地震等这些自然因素的影响，必然会严重影响桥梁的安全和寿命，导致脱层和混凝土开裂，甚至会引发桥梁坍塌，造成不可挽回的经济损失和人员伤亡。
因此，及时检测并修复桥梁的已有损伤，保证服役桥梁在工作时的健康安全的状态，已经成为广大桥梁工程界研究者亟需解决的热门问题之一。由于裂缝是混凝土桥梁病害中破坏较为严重、威胁较大的一种，所以必须对桥梁裂缝进行监测，及时发现并修补，控制裂缝的产生、扩展，将其控制在合理的允许范围内，从而避免桥梁倒塌事故。
本系统针对桥梁裂缝的动态监测系统进行研究，采集桥梁上裂缝的微距图像并实时计算裂缝的宽度，同时监控桥梁上的行车情况，采用多传感器融合的方法，分析桥梁在各种荷载作用下，裂缝的开展和恢复状态，将其控制在安全合理范围内，为桥梁的安全运行服务。其意义有三：1）监测裂缝是否发生变化；2）监测桥上车辆行车状态；3）分析荷载对裂缝的影响。
Smart City
Automatic recycling system for residential units
AP034 »
Our team introduces an automatic classification system for wastes.
If garbage is not classified, it can cause environmental problems, and if it is classified well, resources can be saved through recycling.
Our system can classify not only good-conditioned garbage (plastic, cans, bottles, etc.), but also bad-conditioned garbage.
Data Management
Image and Video Upscaling and Downscaling using FPGA
AP035 »
With time the rate at which we are producing data is increasing at a very tremendous rate. And it seems that this trend will continue with our advancements in technologies and user requirements. One of the big portions of the world’s overall data transmission and data storage is our Videos and Images. Security and surveillance, entertainment, streaming, and roughly every other industry use this kind of data for their applications and the demand for data is increasing than ever. But this increasing demand for data causes two major (global) issues: first, during transmission, they can take a lot of bandwidth of our network, and second that they tend to take a lot of storage space since we need a lot of data points to effectively utilize them for our needs.
Here, this project will be showing the downscaling and upscaling algorithms that are already used in many applications for quite a few years and implementing these algorithms on FPGA and cloud effectively to get the best possible results. Initially, data can be taken from a low-resolution input(or can be downscaled) and then transmitted/stored. When we have to use it, upscaling can be done. These algorithms show very promising results while using fewer resources as to if directly high-resolution input be used from the beginning.
This will give us more bandwidth and storage to work with for our applications, and as computation will be done on FPGA, it will be easily scalable. Edge computing like this will effectively increase productivity and will help in a sustainable advancement in technology. For the continuous advancement in technology and sustainable growth, we have to use our resources efficiently and intelligently and I hope that this project can play a small part in this.
Health
Field Programmable Health
AS026 »
FPGAs have the ability to enhance electromedical applications, including patient monitoring, ventilation, and other life sustainment applications. By using FPGAs and association analog connectivity devices, the provision of such electromedical care can be distributed and reduce the need for concentrated services and their associated transportation and other costs. This project seeks to apply FPGAs to these applications by exploiting integrated sensors and compute capability.
Health
FPGA based Covid-19 detection using Lung Ultrasound Image
AP037 »
With the onset of the Covid-19 pandemic, there has been a tremendous impact on the lives of people globally. The global tally of the no. of infected cases is 229,293,200 and the total death toll is 4,705,498 as of September 20, 2021, and this is just the no. of accounted cases. Apart from this, the covid-19 genome sequence is continuously mutating which resulted in the generation of different and more dangerous types of variants like the delta and delta plus to name a few. The globe witnessed the horrific scenes caused due to the shortage of resources in terms of healthcare during the unprecedented first and second waves. The leading scientists have already predicted the onset of the third wave which is expected to start in the last quarter of the year 2021.
To tackle the third wave, the governments have started the vaccination campaign for the people. But still, as per the earlier predictions the third wave is inevitable and the third world countries that are lagging in terms of medical infrastructure would be affected the most in the oncoming third wave. Reverse Transcription-Polymerase Chain Reaction (RT-PCR) is considered as the standard reference for the Covid-19 based testing but it has certain disadvantages like higher testing costs and longer processing time for the collected samples. Considering the factors like lack of essential infrastructure required for the testing and the capital required, we are hereby proposing a novel approach as a preparatory measure for detecting the presence of Covid-19 using Lung Ultrasound Imaging which in turn can be further deployed on a real-time basis with the help of Intel FPGA Cloud Connectivity Kit and Azure IoT Suite.
Compared to CT scans and X-Ray based detections, Ultrasound Imaging is low cost and radiation-free detection method. In this proposed approach, we would conduct a thorough literature review on the existing deep learning models which has been developed for predicting the covid-19. Based on the earlier approaches, a new model would be proposed which will be extensively trained with available datasets for addressing the limitations offered by the earlier models. Upon the satisfactory performance of the model in terms of accuracy, precision, and computation time, it would be deployed onto the FPGA Cloud Connectivity kit for real-time application. With the help of Azure IoT support, the Ultrasound Images from different centers can be obtained and the test results can be again diverted back to the respective centers at a significantly less amount of time as compared to the RT-PCR test.
This proposed approach will help set up a nationwide/worldwide low-cost testing facility with a significantly less amount of capital being invested. With this approach, we are intending to address the two major challenges prevailing in the present scenario with the first one being high testing costs and improved timing for generating the test results. This approach would in turn be helpful to combat the oncoming third wave of this Covid-19 global pandemic.
Other: 智慧农业
智能盆栽系统设计
PR020 »
智能盆栽系统以FPGA作为硬件平台并辅以各类传感器实时监测盆栽的环境温度、湿度、土壤水分等参数，然后实时做出反应——自动进行浇水、通风等养护动作，以ESP-WROOM-02 WIFI 模块接入Azure IoT Central平台，让用户通过手机进行远程的检测和养护管理。本设计由监测端和控制系统两部分组成。监测端对周围的环境进行监测并采集数据，然后分析温湿度传感器、土壤湿度传感器返回的参数，并在LCD并行24位RGB接口的触摸屏上实时显示各个参数，监测系统实现了对植物生长的实时监测，可以随时查看植物的生长状况。控制系统具有土壤湿度过低时自动浇水的功能、温度过高时自动调节风扇转速的功能、温度过低时自动调节加热板温度的功能、光照不足时自动调节LED植物生长灯亮度的功能。最后，通过 ESP-WROOM-02 WIFI模块将数据上传至网络，接入物联网云平台中，在云平台上实现移动端的功能和界面设计，并在移动端上实现监测和控制的功能，达到远程控制的目的。智能盆栽系统可以视作智能农业大棚的雏形，可以尝试将应用领域扩展至农业以创造更大的价值。
Smart City
导盲小车控制系统设计
PR022 »
导盲小车控制系统设计是指通过导盲系统将周围环境中阻碍盲人出行的障碍物进行检测，通过语音提示盲人安全通过障碍物。导盲小车控制系统，运用超声波模块对周围环境进行实时检测，具有障碍物检测精准，检测效率高等特点，通过语音提示，帮助盲人安全外出。通过电子罗盘，告知盲人应当行进的方向。且结合了物联网+智慧城市的城市发展趋势，与了社会的发展相结合。同将红绿灯信息传给导盲小车，提示盲人安全的通过十字路口。
Health
便携式糖尿病患者饮食监测系统设计
PR023 »
1、光谱数据采集模块：便携式数据采集终端的关键是糖尿病患者摄入食物的近红外光谱数据，由于不同的物质分子结构不同，导致它们选择性吸收不同频率的近红外光，所以物质的近红外光谱中就包含着物质的组成成分等信息。针对糖尿病患者需要知道所摄入食物的含糖量这种情况，需要综合考虑常用的嵌入式技术，同时满足后续近红外光谱数据相应处理算法的精度要求。
2、数据处理模块：由于糖尿病患者饮食监测系统的数据采集终端便携性的要求，所以数据处理模块的选择应结合应用场景的实际需求，既要注意工作效率也要符合经济可靠这一标准。
3、光谱数据处理算法：由于待测物质的近红外光谱与其有机化合物的组成和其分子结构信息存在着对应关系，同时这种对应关系可以通过函数来近似表征。如果使用机器学习中的一些算法来建立起对应的数理函数模型，就可以通过物质的近红外光谱来得到物质的有机化合物组成和分子结构信息。但是针对糖尿病患者需要知道所摄入食物的含糖量这种情况，需要从常用的多元线性回归（MLR）、支持向量机（SVM）、主成分分析（PCA）和偏最小二乘法（PLS）等算法中找出适合本系统的光谱数据处理算法。
Health
FPGA for Healthcare and Wellness
AP041 »
Real-time analysis of medical diagnostics using AI is crucial in healthcare systems. Advanced sensors with deep learning networks need to analyze the diagnostics in set time constraints. The need for high-speed real-time systems is imperative. Generic computer architecture slows down this process. Using low-latency networks with FPGAs will decrease the analysis time by reducing idle cycles, and working on resource utilization. Digital image processing (DIPs) fares better on FPGAs.
Through time, FPGAs are increasingly being used for computationally intensive tasks. Image processing is one such task. To improve the performance of Digital Image Processing systems, it is necessary to implement them on hardware instead of software. FPGAs are inherently good in parallel processing because of the architecture. Incidentally, image processing tasks like feature detection and extractions are highly parallelizable-which makes FPGAs the ideal candidates for this task. We have seen how the healthcare systems had been overwhelmed during the pandemic. Early detection plays a crucial role during the onslaught of highly contagious diseases. Containment and early diagnosis can reduce the losses inflicted during a pandemic. Using the FPGAs and cloud storage, the team can create a robust image detection system to detect the presence of the disease, and use the cloud to distribute relevant information to the concerned doctors. Image detection with CT, MRI, X-Ray scans helps in detecting the disease earlier instead of later. The same image detecting algorithm can be used for similar respiratory diseases that have overlapping symptoms, with minimal changes. Additionally, the cloud ensures that data from across the world is shared with the relevant specialists-removing barriers in healthcare. The accelerometers and sensors will be used to get general information about the body which is important for overall health; for example- temperature sensors to detect temperature, accelerometers for gait analysis and mobility(mobility and gait are important factors to ensure the health and well-being of older patients- fall risk assessment and balance evaluation being a few examples) etc. All the sensors will be used to monitor important aspects of the patient’s health.
This project is a step towards a broad spectrum well-being platform for patients from all walks of life, making healthcare more accessible among the masses.