Other
Reco-LWC: Reconfigurable Lightweight Crypto for IoT applications
AS005 »
NIST announced the finalist who are participating the Lightweight Cryptographic competition for securing small devices which is targeting Internet of Things applications. We are planning to build a reconfigurable processor which runs all of the 10 candidates on FPGA using dynamic reconfiguration. The proposed processor will be evaluated using DE10-Nano Cyclone V SoC FPGA Board
and also Microsoft Azure IoT on both software and hardware perspectives
Other: Hardware Acceleration
輕量型卷積神經網路之硬體加速器設計
PR008 »
本研究提出透過軟硬體協同設計(hardware software co-design)將卷積神經網路(Convolutional Neural Network, CNN)加速器實現於FPGA (Field Programmable Gate Array, FPGA)中的方法。本研究主要分成三個部分來實現：在硬體方面，(1)透過Avalon 匯流排將卷積神經網路模型中的遮罩、權重參數與影像載入至處理單元模組中，進行CNN中卷積與池化的運算，(2)處理單元模組的設計透過平行處理架構提升模組的運算效能，並且透過遮罩與權重的重複使用和定點數運算，有效節省記憶體空間的使用。而在軟體方面，(3)透過NIOS實現CNN中的全連結層，並將此模組與相關硬體模組進行整合，進而實現完整的CNN加速器。從實驗結果可以得知，本文提出的設計架構可以有效提升模型運算效能，並且能夠節省記憶體空間使用量。
Other: Wildlife and forest preservation
iFireFighter
AP006 »
Humanity is currently facing a very major problem, something that has the potential to drastically reduce our population and ruin the lives of our future generations: Climate Change. Due to increased intensity of climate change and lack of any meaningful effort to tackle it, the corresponding problems that accompany the phenomenon of climate change are worsening year by year.
One of these problems that has set the world ablaze are forest fires. The frequency, intensity, and area affected of forest fires are steadily increasing every year. It’s like every year the California wildfires or the Australian bushfires are more intense and cause more damage than the last year’s.
Once a fire has reached critical mass and spread beyond a certain limit, it’s extremely costly, time consuming, and takes a lot of manpower and effort to get it under the control. The natural remedy for tackling this issue is to nip the problem in the bud before it has a chance to blossom.
All major wildfires and forest fires start from a much smaller localized fire that once they reach a critical mass, grow out of control. Our project proposes to detect and alert the relevant authorities about these localized fires before they grow out of control.
Fighting a fire after it has grown past critical mass is extremely costly. From the equipment to the resources to the manpower and personnel, along with the potential for an immense loss of human life, a lot of money must be thrown at the problem to get the fire back under control.
Our project would reduce these costs massively since the focus would then shift from getting a raging uncontrollable fire back under control to quickly and efficiently extinguishing a much smaller fire before it spreads.
Smart City
FPGA Catalyst
AS006 »
Not yet determined
Smart City
(Panotti's Ears) Real time Voice Analytics Co-processor
AP007 »
Speech recognition models have been used extensively on various platforms to provide ease of use, digital smart assistance, and hands-free control. At the cutting edge of this technology, the use of hidden Markov models is common. To improve the computational efficiency of hidden Markov model-based speech recognition systems, various techniques are used, amongst which the Viterbi beam search algorithm is one of the best. However, for large vocabulary speech recognition models with larger beam widths, the beam search algorithm’s sparse matrix operations create a highly constrictive bottleneck. Traditionally GPUs have been used to accelerate such models but with the algorithm's not so parallel nature, GPUs don’t provide an efficient solution and power constraints of IOT devices completely rule them out for Edge level.In this project, we research and formulate an FPGA based Co-processor (RTL level abstraction) to accelerate sparse matrix operation of the beam search algorithm so it can be used on edge devices to revolutionize how we interact with IOT edge level devices.
Other: Agriculture & Water Sustainability
Water Stress Detection using Aerial & Metrological Data(Agri-Bird)
AP008 »
Water is essential in agriculture. Farms use it to grow fresh produce and to sustain their livestock. Major environmental functions and human needs critically depend on water. In regions of the world affected by water scarcity economic activities can be constrained by water availability, leading to competition both among sectors and between human uses and environmental needs.
According to a 2017-18 government survey, agriculture contributes to 18.9% of the GDP and uses up 42.3% of the labor force in Pakistan. But with agriculture using up about 90% of Pakistan’s water supply, and Pakistan’s water crisis threatening to exhaust the country’s water resources by 2040, there is a dire need for solutions that help in the efficient use of water in agriculture, and farming in particular.
To combat this problem and provide a sustainable mechanism to farmers, we propose an aerial collection and soil-sampling data framework that will lead to sustainable, precise, secure, and efficient farming. Our solution will focus on the water-stress or drought-stress of plants.
Water stress refers to the water deficit in plants and has shown to be a very useful piece of information in farming. In addition to being a good predictor for the yield of the plantation, water stress also allows us to respond timely to areas that are under-watered or over-watered. Of course, water stress is most valuable as information for planning irrigation, but it can also be a very decent measure of areas that are at risk of wildfires.
Our solution proposes to mount an FPGA to the aerial unit where it will be collecting data with the help of modules, subsequently process it on the edge, and then transfer all the relevant data to the cloud for further processing and analysis. In order to give our results more credibility, we will also be collecting some soil-sampled data and combining it with the aerial data to give us our final results in the cloud. Our results will aim to give accurate predictions, useful suggestions to farmers, routing data for irrigation channels, and warnings for risks and disasters.
Industrial
FPGA based Wireless Sensor Networks
AP009 »
Wireless Sensor Networks (WSN) implementation on FPGA connecting multiple nodes with different sensors from analog devices and actuators to make industrial work more independent of human control physically. The data management will be made possible using Microsoft Azure cloud services, making the data accessible as and when required to the controller and the specifications can be tuned at admin end. This project is to make cloud based applications on FPGA and to explore new emerging technologies to make them system independent using FPGA.
Data Management
Cactus Fish
AS007 »
Utilizing Microsoft Azure to implement IoT solutions on DE10
Smart City
Model-Predictive-Traffic-Mangement
AP010 »
We are proposing to build a hardware platform that's able to identify individual vehicles in real-time. The device will be connected to a real-time traffic model that's run on the cloud. Then by placing this device at a strategic location in a city, over time, we would be able to build a very accurate predictive traffic model.
Overall, we expect our project (hardware platform together with the back-end cloud-based software) will provide much better tools for city authorities to manage traffic than what's available today.
Smart City
Smarthome Control System
PR009 »
Smart home has become a trend of housing in the future. At present, most smart home control systems are closed systems developed by major developers. With specific interfaces and communication protocols, they cannot be well managed when they have problems like not receiving real-time data from sensors when there are too many devices.
The design is intended to implement smart home control and data acquisition in the house covered with Wi-Fi with FPGA Cloud Connectivity Kit platform based on DE10-Nano. Data monitoring and smart home remoting control functions are implemented with Microsoft Azure IoT and AI tools in cloud.
Water Related
Smart water monitoring, protection of pastoral and agricultural areas on dry-lands
EM005 »
With climate change and water scarcity, arid countries' policies aim to conserve dams’ water for domestic and industrial use only. Due to a lack of budget, having no other alternative, Small-farms crop production turns to the use of innovative low-cost solutions for irrigation and livestock. The proposed project outlines a new approach supporting agricultural agencies and policies at all levels, livestock professionals, smallholding farmers, and local populations, to stabilize the ecologically unsustainable exploitation of the water on dry-lands. The proposed approaches aim to implement Edge Artificial Intelligence on Intel FPGA and Microsoft Azure cloud computing for the prediction of water quality and its evolution, manage an innovative irrigation process, livestock watering points, as well as artisan activities (pottery...).
Health
Phase dependent retina stimulation
EM006 »
We try to build a FPGA based solution to stimulate retinal tissue phase dependently of the overlaying local field potentials. By this we want to increase the stimulation efficiency and thus decrease the power consumption of the device.