FitNN: A Low-resource FPGA-based CNN Accelerator for Drones

Executing deep neural networks (DNNs) on resource-constraint edge devices, such as drones, offers low inference latency, high data privacy, and reduced network traffic. However, deploying DNNs on such devices is a challenging task. During DNN inference, intermediate results require significant data movement and frequent off-chip memory (DRAM) access, which decreases the inference speed and power efficiency. To address this issue, this paper presents a field-programmable gate array (FPGA)-based convolutional neural network (CNN) accelerator, named FitNN, which improves the speed and power efficiency of CNN inference by reducing data movements. FitNN adopts a pre-trained CNN of iSmart2, which is composed of depthwise and pointwise blocks in the Mobilenet structure. A cross-layer dataflow strategy is proposed to reduce off-chip data transfer of feature maps. Also, multi-level buffers are proposed to keep the most needed data on-chip (in block RAM) and avoid off-chip data reorganization and reloading. Finally, a computation core is proposed to operate the depthwise, pointwise, and max-pooling computation as soon as the data arrives without reorganization, which suits the real-life scenario of the data arriving in sequence. In our experiment, FitNN is implemented on two FPGA-based platforms (both at150 MHz), Ultra96-V2 and PYNQ-Z1, for drone-based object detection with batch size=1. The results show that FitNN achieves 15 frames per second (FPS) on Ultra96-V2, with power consumption of 4.69 W. On PYNQ-Z1, FitNN achieves 9 FPS with 1.9 W of power consumption. Compared with the previous FPGA-based implementation of iSmart2 CNN, FitNN increases the efficiency (FPS/W) by 2.37 times

Design and simulation of a high isolation RF MEMS shunt capacitive switch for C-K band

Design and simulation of a high isolation RF MEMS shunt capacitive switch for C-K ban

Plasma Process. Polym. 10/2012

Plasma Process. Polym. 10/201

A Probabilistic Model for Minimization of Solar Energy Operation Costs as Well as CO2 Emissions in a Multi-Carrier Microgrid (MCMG)

This paper proposes a probabilistic model with the aim to reduce the solar energy operation cost and CO2 emissions of a multi-carrier microgrid. The MCMG in this study includes various elements such as combined heat and power (CHP), electrical heat pump (EHP), absorption chiller, solar panels, and thermal and electrical storages. A MILP model is proposed to manage the commitment of energy producers, energy storage equipment, the amount of selling/buying of energy with the upstream network, and the energy consumption of the responsible electrical loads for the day-ahead optimal operation of this microgrid. The proposed operation model is formulated as a multi-objective optimization model based on two environmental and economic objectives, using a weighted sum technique and a fuzzy satisfying approach. In this paper, the 2 m + 1-point estimate strategy has been used to model the uncertainties caused by the output power of solar panels and the upstream power supply price. In order to evaluate the performance of the proposed model, and also for minimizing cost and CO2 emissions, the simulation was conducted on two typical cold and hot days. Numerical results show the proposed model’s performance and the effect of electrifying the heating and cooling of the microgrid through the EHP unit on greenhouse gas emissions in the scenarios considered

Closed-loop Control Systems for Pumps used in Portable Analytical Systems

The demand for accurate control of the flowrate/pressure in chemical analytical systems has given rise to the adoption of mechatronic approaches in analytical instruments. A mechatronic device is a synergistic system which combines mechanical, electronic, computer and control components. In the development of portable analytical devices, considering the instrument as a mechatronic system can be useful to mitigate compromises made to decrease space, weight, or power consumption. Fluid handling is important for reliability, however, commonly utilized platforms such as syringe and peristaltic pumps are typically characterized by flow/pressure fluctuations and slow responses. Closed loop control systems have been used effectively to decrease the difference between desired and realized fluidic output. This review discusses the way control systems have been implemented for enhanced fluidic control, categorized by pump type. Advanced control strategies used to enhance the transient and the steady state responses are discussed, along with examples of their implementation in portable analytical systems. The review is concluded with the outlook that the challenge in adequately expressing the complexity and dynamics of the fluidic network as a mathematical model has yielded a trend towards the adoption of experimentally informed models and machine learning approaches

A Bluetooth-Enabled Device for Real-Time Detection of Sitting, Standing, and Walking: Cross-Sectional Validation Study

Background This study assesses the accuracy of a Bluetooth-enabled prototype activity tracker called the Sedentary behaviOR Detector (SORD) device in identifying sedentary, standing, and walking behaviors in a group of adult participants. Objective The primary objective of this study was to determine the criterion and convergent validity of SORD against direct observation and activPAL. Methods A total of 15 healthy adults wore SORD and activPAL devices on their thighs while engaging in activities (lying, reclining, sitting, standing, and walking). Direct observation was facilitated with cameras. Algorithms were developed using the Python programming language. The Bland-Altman method was used to assess the level of agreement. Results Overall, 1 model generated a low level of bias and high precision for SORD. In this model, accuracy, sensitivity, and specificity were all above 0.95 for detecting sitting, reclining, standing, and walking. Bland-Altman results showed that mean biases between SORD and direct observation were 0.3% for sitting and reclining (limits of agreement [LoA]=–0.3% to 0.9%), 1.19% for standing (LoA=–1.5% to 3.42%), and –4.71% for walking (LoA=–9.26% to –0.16%). The mean biases between SORD and activPAL were –3.45% for sitting and reclining (LoA=–11.59% to 4.68%), 7.45% for standing (LoA=–5.04% to 19.95%), and –5.40% for walking (LoA=–11.44% to 0.64%). Conclusions Results suggest that SORD is a valid device for detecting sitting, standing, and walking, which was demonstrated by excellent accuracy compared to direct observation. SORD offers promise for future inclusion in theory-based, real-time, and adaptive interventions to encourage physical activity and reduce sedentary behavior