Abstract: Connectivity between a radio-based application pipeline processing server and a control plane of a provider network is verified. Based on requests received at the control plane, a first isolated request handler, a second isolated request handler and an offloading manager are launched at the server. The offloading manager causes a first network function for which a request is received from the first request handler to be executed at a first network function accelerator of the server, and a second network function for which a request is received from the second request handler to be executed at a second network function accelerator of the server.

Abstract: An automated server-based scheme allows a hosted service such as an individual computer system, computer application, or network service, to infer root certificate authorities present in client trust stores. Presenting a random selection of one or more digital certificates to a given client seeking a favorite icon URL, in response to a Transport Layer Security (TLS) connection request from the client, allows the service operator's trained machine learning system to define a customized set of potentially-trusted root certificate authorities based on learned successes and failures of connections with the given client, as well as other parties. The hosted service operator may employ a certificate manager and/or client-specific rules for presenting the certificates.

Abstract: A compute channel can have multiple computational circuit blocks coupled in series to form a pipeline. The compute channel can perform a computation on an input tensor to generate an output tensor based on an instruction. When the computational does not require all of the computational circuit blocks, the throughput of the compute channel can be increased by splitting the data elements of the input tensor into multiple input data streams. The multiple input data streams are provided to respective subsets of one or more computational circuit blocks in the pipeline using bypass circuitry of the computational circuit blocks, and the computation can be performed on multiple input data streams in the respective subsets of one or more computational circuit blocks to generate multiple output data streams corresponding to the output tensor.

Abstract: Devices and techniques are generally described for retrieving three dimensional part models. Two-dimensional (2D) image data representing an object with at least a first part and a second part may be received. A first machine learning model may be used to generate a first shape embedding representing the first part and a second shape embedding representing the second part. A first 3D model stored in a non-transitory computer-readable memory that represents the first part may be determined based at least in part on the first shape embedding. A second 3D model stored in the non-transitory computer-readable memory that represents the second part may be determined based at least in part on the second shape embedding.

Abstract: A quantum programming environment may include an assisted composition system to assist the composition of quantum objects. The assisted composition system may receive a partial portion of a quantum object that is being composed but not yet fully composed by a user. The assisted composition system may determine a first abstract representation of the partial portion of the quantum object being composed. The assisted composition system may determine that the first abstract representation resembles at least a first portion of a second abstract representation of a stored quantum object stored in a library for the quantum programming environment. The assisted composition system may obtain a second portion of the stored quantum object from the library and provide it to the user as a next portion to the partial portion of the quantum object being composed.

Abstract: Disclosed are various embodiments for identifying fault injection impact zones. A test for an application can be received from a test service. An application schema can then be created that represents the systems or services that are involved in the execution of the application. Applications with a similar application schema are then identified using a first machine-learning model. A subset of the plurality of applications that have experienced a similar test are then identified with a second machine-learning model. Log files associated with each of the subset of applications are then analyzed to identify errors that occurred during the similar test. Each of the identified errors are then provided to the test service in response to receipt of the test.

Abstract: The present invention features new waveguide layouts for input, redirection (expansion), and output holograms that minimize cross talk between colors and allow all three colors to reside in a single waveguide. The use of multiple incoupling holograms that diffract different colors of light in different directions, or along different paths, through a waveguide substrate advantageously provides for a reduction of cross-talk between the colors of a holographic image. In a square-shaped design, red, green, and blue input and output holograms approximately overlay on top of each other. The green redirection hologram is laterally separated from the red and blue redirection holograms. Using this square-shape design, the light beams for the three colors are separated into two paths propagating from input to output holograms.

Abstract: Techniques for machine learning prediction using edge devices are described. In some examples a method of use includes receiving a request to load a second model onto an edge device while a first model has already been loaded on the edge device, wherein the second model and the first model share an external handle; loading at least one instance the second model into memory of the edge device; and after the second model has been loaded into memory of the edge device, directing a prediction request to the shared external handle to the second model instead of the first model.

Abstract: A system and method for authorization policy validation. A validator takes as input an authorization policy to be analyzed and a schema that specifies entity types and their attributes, types of entity parents in an entity hierarchy, and which entity types can be used with which actions. The validator checks that the policy conforms to the schema. If the check passes, then the policy is guaranteed to be free of both type errors and attribute access errors for any input that conforms to the schema.

Abstract: Some embodiments provide a compiler for optimizing the implementation of a machine-trained network (e.g., a neural network) on an integrated circuit (IC). The compiler of some embodiments receives a specification of a machine-trained network including multiple layers of computation nodes and generates a graph representing options for implementing the machine-trained network in the IC. In some embodiments, the compiler also generates instructions for gating operations. Gating operations, in some embodiments, include gating at multiple levels (e.g., gating of clusters, cores, or memory units). Gating operations conserve power in some embodiments by gating signals so that they do not reach the gated element or so that they are not propagated within the gated element. In some embodiments, a clock signal is gated such that a register that transmits data on a rising (or falling) edge of a clock signal is not triggered.

Abstract: This disclosure describes, in part, techniques for conserving power on an electronic device. For instance, at given time intervals, the electronic device may be sending input data to a network device and receiving audio data from the network device. The electronic device may then use one or more techniques to determine when to switch from operating in a first mode, where the electronic device sends and/or receives the data, to operating in a second mode, where the electronic device ceases sending and/or receiving the data. For example, the electronic device may make the determination based on an amount of data stored in a buffer, whether the electronic device receives data, using data received from the network device, and/or the like. Based on the determination, the electronic device may switch to the second mode in order to conserve power.

Abstract: A multi-column index is generated based on an interleaving of data bits for selectivity for efficient processing of data in a relational database system. Two or more columns may be identified for inclusion in the multi-column index for a relational database table. Based, at least in part, on the interleaving of data bits for selectivity from the identified columns, a multi-column index is generated for the relational database table that provides a respective index value for each entry in the relational database table. The entries of the relational database table may then be stored according to the index values of the multi-column index.

Abstract: Techniques for predicting an answer to a question using a machine learning model are described. In some examples, the model predicts one or more answers to the question by: predicting at least two answers to the question using a first component of the question-answer model from a set of passages, generating, using a second component of the question-answer model, at least one question for each of the predicted at least two answers, and performing roundtrip predictions until each generated question only has one answer.

Abstract: A first distributed unit (DU) of a radio-based application determines that traffic associated with a particular user equipment device (UED) is to be handed over to a second DU. The first DU transmits contents of a buffer, including a data block transmitted from the UED, to the second DU using a direct network channel established between the DUs. The second DU stores the contents in a second buffer, and uses the contents to perform a retransmission operation associated with the traffic of the UED.

Abstract: Session-specific edit recommendations may be made for editing a code file. After a code editing session is started, code file edits may be captured. A machine learning technique may be applied to learn the code edits and recommend alternative code portions for portions of the code file during the code editing session. The recommendations may be provided and accepted, or not, via an interface of a code editor application.

Abstract: To reduce the storage size of weight tensors and speed up loading of weight tensors from system memory, a compression technique can be employed to remove zero values from a weight tensor before storing the weight tensor in system memory. A sparsity threshold can be enforced to achieve a compression ratio target by forcing small weight values to zero during training. When the weight tensor is loaded from system memory, a direct memory access (DMA) engine with an in-line decompression unit can decompress the weight tensor on-the-fly. By performing the decompression in the DMA engine, expansion of the weight values back to the original weight tensor size can be carried out in parallel while other neural network computations are being performed by the processing unit.

Abstract: Techniques for understanding classification by a machine learning model are described. A system determines a portion of a user input that causes a machine learning model to classify the user input to a particular classification. The system may generate output data according to the determined portion of the user input. In some cases, the system may generate, using the determined portion, output data explaining processing performed with respect to the user input. In other cases, the system may generate output data requesting feedback. In yet other cases, the system may change a domain, an intent or other classification determined for the user input based on the determined portion.

Abstract: Systems and methods for device integration in augmented reality environments are disclosed. For example, prior to an AR session being initiated, smart devices within a physical environment where the AR session is to be utilized may be identified and selected based on the functionality of the smart devices and the capabilities of an AR application associated with the AR session. Location information about the smart devices may also be determined and this information may be utilized to generate configuration data, which may be used by the AR application to cause device state transitions on the smart devices in response to events that occur in an AR environment of the AR session.

Abstract: Embodiments describe techniques for controlling picking robots to retrieve items on the floor and return them to an appropriate location. In one embodiment, the picking robots wait until a sensor in a monitored area (e.g., a designated robot area or an area around a pick/stow station) detects a dropped item on the floor. A central control system then dispatches the picking robot to the location to retrieve the dropped item. In another embodiment, when not being dispatched, the picking robots patrol the area looking for dropped items.