"1. Automatically generate Verilog code tools based on the hardware architecture of convolutional neural networks: 4 different hardware architectures (output stationary, weight stationary, Tree architecture, NVDLA) can be generated for the currently more commonly used DNN networks. 2. Visual performance index analysis tool: According to the selected DNN model,the choice of hardware architecture specifications, analyze the performance index."

This study explores the energy-saving efficiency of a complex water-cooled chiller system composed of multiple chillers, cooling towerswater pumps connected by circulation pipelines. The deep learning modeling method driven by big data provides optimized parameters of the water-cooled chiller system to reduce the power consumption of the chiller system. Experiments show that this technology can increase the power saving efficiency of AU Optronics Longtan L5B factory by 2.42. This method has been implemented in many factories of AUO.

This technique employs AIbig data analytics to precisely forecast cooling load demandestimate efficiency of different chiller combinations. Time-of-Use Pricingoptimal chiller load interval are also considered for practical needs. Decision supports of optimal chiller configuration are provided to enhance energy conservation.

This technique combines more than one AI models to precisely predict current under noisy data from current sensoroptimizes speed rate based on this AI forecasting model. At the same time, it considers the practical requirementslimitations of workpiece surface roughness, tool machine current load, etc. This technique provides decision supports for optimizing parameters of CNC milling machine to keep high efficiencyenhance energy conservation.

"1. Use PE Cluster to make the calculation reconfigurable,can be modularized into 36X PEs, supporting up to 2160PEs 2. It can be configured as an independent IP with high computing throughputlow power consumption. The computing performance can reach 864 GOPS@200MHz when 2160PEs are employed."

We develop an image-based sim-to-real transfer technique for deep reinforcement learning. First, we train a teacher model to move along a near optimal path. We then use this model to teach a student model the correct actions along with randomization. The technique bridges the sim-to-real gap, improving the driving speedrobustness of the simulator-trained student model in the real world.

This technique employs AIbig data analytics to precisely forecast cooling load demandestimates the efficiency of different chiller combinations. Time-of-Use Pricingoptimal chiller load-interval are also considered for practical needs. Decision supports of optimal chiller configuration is provided to enhance energy conservation. Moreover, using real data feedbackmodel health examination, the model can do self-calibrationmake evolution, continuously learn from the experts,provide decision supports in good quality.

This technology＇s nanofluid/ultrasonic atomization micro-lubrication system is different from the general micro-lubrication technology that uses high-pressure air atomization. It is first to use cavitation characteristics to disperse a small amount of nanofluid into microscopic particleseffectively disperse the bondingagglomeration of nanoparticles due to the Van der Waals force. The second stage of atomization is carried out through high-pressure air. The oil mist particles of the nanofluid can easily enter the micro-machining cutting area for coolinglubrication.

This technology contains two important sub-technologies. The first is the development of ultra-high performance concrete composite materials, mastering the 3D printing related properties,developing printable concrete that can actually be used for printing. The second is the simulation, analysisoptimization technology of the concrete 3D printing process. The infinite element method developed by ourselves is used to simulateanalyze the concrete 3D printing process,the geometric of the object is considered to optimize the printing pathparameters.