An Energy-Efficient High-Throughput Mesh-Based Photonic On-Chip Interconnect for Many-Core Systems

Future high-performance embedded and general purpose processors and systems-on-chip are expected to combine hundreds of cores integrated together to satisfy the power and performance requirements of large complex applications. As the number of cores continues to increase, the employment of low-power and high-throughput on-chip interconnect fabrics becomes imperative. In this work, we present a novel mesh-based photonic on-chip interconnect, named PHENIC-II, for future high-performance many-core systems. The novel architecture is based on an energy-efficient non-blocking photonic switch and a contention-aware routing algorithm. Simulation results show that the proposed system provides better bandwidth and energy efficiency when compared to conventional hybrid photonic NoC systems

Hybrid Silicon-Photonic Network-on-Chip for Future Generations of High-performance Many-core Systems

International audiencePhotonic Networks-on-Chip (PNoCs) promise significant advantages over their electronic counterparts. In particular, they offer a potentially disruptive technology solution with fundamentally low power dissipation that remains independent of capacity while providing ultra-high throughput and minimal access latency. In conventional hybrid PNoC systems, several electrical control functions, such as path setup, acknowledgment and tear-down are necessary for the end-to-end optical transfer. However

Real-time Hand-Gesture Recognition based on Deep Neural Network

Hand gestures are a kind of nonverbal communication in which visible bodily actions are used to communicate important messages. Recently, hand gesture recognition has received significant attention from the research community for various applications, including advanced driver assistance systems, prosthetic, and robotic control. Therefore, accurate and fast classification of hand gesture is required. In this research, we created a deep neural network as the first step to develop a real-time camera-only hand gesture recognition system without electroencephalogram (EEG) signals. We present the system software architecture in a fair amount of details. The proposed system was able to recognize hand signs with an accuracy of 97.31%

Robust Vehicle-to-Grid Energy Trading Method Based on Smart Forecast and Multi-Blockchain Network

In the present era, energy issues are a significant concern, and the energy trading market is the crucial sector to facilitate supply-demand balance and sustainable development. For better demand response and grid balancing, vehicle-to-grid (V2G) technology is rapidly gaining importance in energy markets. To narrow the gap between ideal V2G goals and actual applications needs, energy trading system has to overcome the challenges of over-centralized structure, inflexible timeline adaptation, limited market scale and energy efficiency, excessive feedback time costs, and low rate of economic return. To address these issues and ensure a secure energy market, we propose a decentralized intelligent V2G system called V2G Forecasting and Trading Network (V2GFTN) to achieve efficient and robust energy trading in campus EV networks. A multiple blockchain structure is proposed in V2GFTN to ensure trading security and data privacy between energy requests and offers. V2GFTN also integrates energy forecasting functions for EVs with a smart energy trading and EV allocation mechanism called SRET so that the EVs with driving tasks can supply their extra power back to the grid and achieve higher energy efficiency and economic profit. Through rigorous experimentation and compared with equivalent studies, V2GFTN system has demonstrated higher economic profit and energy demand fill rate by up to 1.6 times and 1.9 times than the state-of-the-art V2G approaches

Design and Optimization of a Deep Neural Network Architecture for Traffic Light Detection

Autonomous Driving has recently become a research trend and efficient autonomous driving system is difficult to achieve due to safety concerns, Applying traffic light recognition to autonomous driving system is one of the factors to prevent accidents that occur as a result of traffic light violation. To realize safe autonomous driving system, we propose in this work a design and optimization of a traffic light detection system based on deep neural network. We designed a lightweight convolution neural network with parameters less than 10000 and implemented in software. We achieved 98.3% inference accuracy with 2.5 fps response time. Also we optimized the input image pixel values with normalization and optimized convolution layer with pipeline on FPGA with 5% resource consumption