Abstract: In an example, an apparatus comprises logic, at least partially including hardware logic, to save one or more outputs of a deep learning neural network in a storage system of an autonomous vehicle and upload the one or more outputs to a remote server. Other embodiments are also disclosed and claimed.

Abstract: In an example, an apparatus comprises a plurality of execution units; and logic, at least partially including hardware logic, to determine a sub-graph of a network that can be executed in a frequency domain and apply computations in the sub-graph in the frequency domain. Other embodiments are also disclosed and claimed.

Abstract: Program execution coverage is expanded by selectively capturing inputs and execution state information in a production environment after identifying test insufficiencies in a test environment. A program being tested has a non-covered portion and a covered portion. The non-covered portion includes code which is not exercised by a set of tests with regard to an execution coverage, e.g., path coverage, statement coverage, Boolean condition coverage, etc. A non-covered portion identifier identifies the non-covered portion, e.g., by noting breakpoint misses or static analysis constraint solution computational requirements. A trigger inserter inserts a trigger in the program, at a position which permits capture of data which aids exercise of the non-covered portion. A data capturer captures such execution coverage expansion data (“ECE data”) in response to trigger activation.

Abstract: In an example, an apparatus comprises a plurality of execution units; and logic, at least partially including hardware logic, to determine a sub-graph of a network that can be executed in a frequency domain and apply computations in the sub-graph in the frequency domain. Other embodiments are also disclosed and claimed.

Abstract: A method and system for detecting vulnerable wireless devices operating in a wireless environment of an organization are provided. The method includes identifying a plurality of wireless devices operable in the wireless environment; for each identified wireless device: receiving intercepted traffic transmitted by the wireless device, wherein the intercepted traffic is transported using at least one type of wireless protocol; analyzing the intercepted traffic to determine if the wireless device is vulnerable, wherein the analysis is performed using an at least one investigation action; computing a risk score based on results of each of the least one investigation action; determining, based on the computed risk scores, if the wireless device is as vulnerable; and generating an alert, when it is determined that the wireless device is vulnerable.

Abstract: Certain embodiments disclosed herein include a method for detecting potential vulnerabilities in a wireless environment. The method comprises collecting, by a network sensor deployed in the wireless environment, at least wireless traffic data; analyzing the collected wireless traffic data to detect at least activity initiated by a wireless entity in the wireless environment; initiating at least one investigation actions to determine if any identified wireless network is a vulnerable network; determining a risk score based in part on the at least one investigation action; and enforcing a security policy on the identified vulnerable network, wherein the security policy is determined responsive to the risk score and instructions received from a control system.

Abstract: A method and system for detecting vulnerable wireless devices operating in a wireless environment of an organization are provided. The method includes identifying a plurality of wireless devices operable in the wireless environment; for each identified wireless device: receiving intercepted traffic transmitted by the wireless device, wherein the intercepted traffic is transported using at least one type of wireless protocol; analyzing the received traffic to determine if the wireless device is vulnerable, wherein the analysis is performed using at least a profile generated for the wireless device; and generating an alert, when it is determined that the wireless device is vulnerable.

Abstract: Certain embodiments disclosed herein include a method for detecting potential vulnerabilities in a wireless environment. The method comprises collecting, by a network sensor deployed in the wireless environment, at least wireless traffic data; analyzing the collected wireless traffic data to detect at least activity initiated by a wireless entity in the wireless environment; sending, to a control system, data indicating the detected wireless entity; and enforcing a security policy on the detected wireless entity based on instructions received from the control system.

Abstract: Certain embodiments disclosed herein include a method for detecting potential vulnerabilities in a wireless environment. The method comprises collecting, by a network sensor deployed in the wireless environment, at least wireless traffic data; analyzing the collected wireless traffic data to detect at least activity initiated by a wireless entity in the wireless environment; sending, to a control system, data indicating the detected wireless entity; and enforcing a security policy on the detected wireless entity based on instructions received from the control system.

Abstract: In an example, an apparatus comprises a plurality of execution units; and logic, at least partially including hardware logic, to determine a sub-graph of a network that can be executed in a frequency domain and apply computations in the sub-graph in the frequency domain. Other embodiments are also disclosed and claimed.

Abstract: In an example, an apparatus comprises logic, at least partially including hardware logic, to save one or more outputs of a deep learning neural network in a storage system of an autonomous vehicle and upload the one or more outputs to a remote server. Other embodiments are also disclosed and claimed.

Abstract: A mechanism is described for facilitating memory handling and data management in machine learning at autonomous machines. A method of embodiments, as described herein, includes detecting multiple tables associated with multiple neural networks at multiple autonomous machines, where each of the multiple tables include an index. The method may further include combining the multiple tables and multiple indexes associated with the multiple tables into a single table and a single index, respectively, where the single table is communicated to the multiple autonomous machines to allow simultaneous processing of one or more portions of the single table using one or more memory devices and one or more processors of one or more of the multiple autonomous machines.

Abstract: In an example, an apparatus comprises a plurality of execution units and logic, at least partially including hardware logic, to implement training of a deep tree application at a data center. Other embodiments are also disclosed and claimed.

Abstract: In an example, an apparatus comprises a plurality of execution units comprising at least a first type of execution unit and a second type of execution unit and logic, at least partially including hardware logic, to expose embedded cast operations in at least one of a load instruction or a store instruction; determine a target precision level for the cast operations; and load the cast operations at the target precision level. Other embodiments are also disclosed and claimed.

Abstract: In an example, an apparatus comprises a plurality of execution units and logic, at least partially including hardware logic, to generate synthetic data for a generative adversarial network (GAN) using the plurality of execution units. Other embodiments are also disclosed and claimed.

Abstract: In an example, an apparatus comprises at least one execution platform; and logic, at least partially including hardware logic, to receive a trained neural network model in a model optimizer and convert the trained neural network model to an optimized model comprising parameters that are fit to the at least one execution platform. Other embodiments are also disclosed and claimed.

Abstract: In an example, an apparatus comprises a plurality of execution units comprising and logic, at least partially including hardware logic, to receive a plurality of data inputs for training a neural network, wherein the data inputs comprise training data and weights inputs; represent the data inputs in a first form; and represent the weight inputs in a second form. Other embodiments are also disclosed and claimed.

Abstract: In an example, an apparatus comprises a plurality of execution units; and logic, at least partially including hardware logic, to determine a sub-graph of a network that can be executed in a frequency domain and apply computations in the sub-graph in the frequency domain. Other embodiments are also disclosed and claimed.

Abstract: In an example, an apparatus comprises logic, at least partially including hardware logic, to implement a lossy compression algorithm which utilizes a data transform and quantization process to compress data in a convolutional neural network (CNN) layer. Other embodiments are also disclosed and claimed.

Abstract: A method and system for detecting vulnerable wireless devices operating in a wireless environment of an organization are provided. The method includes identifying a plurality of wireless devices operable in the wireless environment; for each identified wireless device: receiving intercepted traffic transmitted by the wireless device, wherein the intercepted traffic is transported using at least one type of wireless protocol; analyzing the received traffic to determine if the wireless device is vulnerable, wherein the analysis is performed using at least a profile generated for the wireless device; and generating an alert, when it is determined that the wireless device is vulnerable.