The invention relates to a traffic signal control field, discloses method and system of dynamic adjust signal time according to traffic flows in order to decrease stops/starts and green-light idle time: method includes: 1) get parameters of roadnet, signals; 2) get traffic flows; 3) I-neurons predict traffic flows about over thresholds; 4) P-neuron determine pre-judges according to over thresholds of intersection; 5) overall trade-off accept/reject, priority, schedule, and management of pre-judges, make and send I-instructions; system includes: 1) predict method package; 2) traffic data center or vehicle queue detecting equipments; 3) or vehicle in/out detectors; 4) traffic signals controllers. The predicting math model based signals net universal “A-A” serial method, support String mode control, enable roadnet traffic always run low energy consumption signals, avoid redundant stops/starts one time per period per vehicle per road-segment about 60 seconds and idle gasoline consumption, 30 vehicles about 30 minutes idle gasoline consumption per road-segment, and with solitary wave technique for dissolving jam-core, suddenly-happened big queue, provide a serial continuity solution means for signal control to dissolve congestion core, early congestion, delay arrival of a large cluster of congestion, improve efficiency of traffic signal response.

A fractal computing device according to an embodiment of the present application may be included in an integrated circuit device. The integrated circuit device includes a universal interconnect interface and other processing devices. The calculating device interacts with other processing devices to jointly complete a user specified calculation operation. The integrated circuit device may also include a storage device. The storage device is respectively connected with the calculating device and other processing devices and is used for data storage of the computing device and other processing devices.

An example includes a sequence generator to generate a plurality of sequence pairs, a first one of the sequence pairs including: (i) a first input sequence representing first accesses to first tensors in a first loop nest of a first computer program, and (ii) a first output sequence representing a first tuned loop nest corresponding to the first accesses to the first tensors in the first loop nest; a model trainer to train a recurrent neural network based on the sequence pairs as training data, the recurrent neural network to be trained to tune loop ordering of a second computer program based on a second input sequence representing second accesses to a second tensor in a second loop nest of the second computer program; and a memory interface to store, in memory, a trained model corresponding to the recurrent neural network.

A compiler can receive a computation workload, and a description of the computation graph of the workload and compile a circuit layout of the workload. In one embodiment, an RTL generator assigns the node operations of the computation graph to a first or second type. In the first type, the workload is loaded and processed in tiles equal to a compute filter width. In the second type, the workload is loaded in tiles larger in size than the width of the compute filter, allowing the compute filter to process more operations in parallel and reach the data needed for the underlying operations more efficiently.

Systems and methods for tone mapping of high dynamic range (HDR) images for high-quality deep learning based processing are disclosed. In one embodiment, a graphics processor includes a media pipeline to generate media requests for processing images and an execution unit to receive media requests from the media pipeline. The execution unit is configured to compute an auto-exposure scale for an image to effectively tone map the image, to scale the image with the computed auto-exposure scale, and to apply a tone mapping operator including a log function to the image and scaling the log function to generate a tone mapped image.

Transportation systems have artificial intelligence including neural networks for recognition and classification of objects and behavior including natural language processing and computer vision systems. The transportation systems involve sets of complex chemical processes, mechanical systems, and interactions with behaviors of operators. System-level interactions and behaviors are classified, predicted and optimized using neural networks and other artificial intelligence systems through selective deployment, as well as hybrids and combinations of the artificial intelligence systems, neural networks, expert systems, cognitive systems, genetic algorithms and deep learning.

A device for obtaining a local optimal AI model may include an artificial intelligence (AI) chip and a processing device configured to receive a first initial AI model from the host device. The device may load the initial AI model into the AI chip to determine a performance value of the AI model based on a dataset, and determine a probability that a current AI model should be replaced by the initial AI model. The device may determine, based on the probability, whether to replace the current AI model with the initial AI model. If it is determined that the current AI model be replaced, the device may replace the current AI model with the initial AI model. The device may repeat the above processes and obtain a final current AI model. The device may transmit the final current AI model to the host device.

Disclosed herein are generative manifold networks (GMNs) which enable accurate prediction and modeling of complex interconnected systems.

Transaction-enabled methods for providing provable access to a distributed ledger with a tokenized instruction set for polymer production processes are described. A method may include accessing a distributed ledger comprising an instruction set for a polymer production process and tokenizing the instruction set. The method may further include interpreting an instruction set access request and providing a provable access to the instruction set. The method may further include providing commands to a production tool of the polymer production process and recording the transaction on the distributed ledger.

A method for classifying images of oligolayer exfoliation attempts. In some embodiments, the method includes forming a micrograph of a surface, and classifying the micrograph into one of a plurality of categories. The categories may include a first category, consisting of micrographs including at least one oligolayer flake, and a second category, consisting of micrographs including no oligolayer flakes, the classifying comprising classifying the micrograph with a neural network.