Abstract: In one embodiment, a computer-implemented method performed by a data processing (DP) accelerator, includes receiving, at the DP accelerator, an artificial intelligence (AI) model that has been previously trained and a set of input data from a host processor; receiving, at the DP accelerator, a watermark kernel from the host processor; executing the watermark kernel within the DP accelerator on the AI model and the set of input data. The watermark kernel, when executed, is configured to: generate a new watermark by inheriting an existing watermark from a data object of the set of input data or the AI model, perform an AI inference using the AI model based on the input data to generate output data, and implant the new watermark within the output data. The DP accelerator then transmits output data having the new watermark implanted therein to the host processor.

Abstract: An apparatus of rapid-positioning with curved light surface includes a transmitter and a receiver. The transmitter can emit an optical signal to the receiver. The receiver can receive an optical signal emitted by the transmitter. The apparatus of rapid-positioning with curved light surface determines a position of the receiver according to the optical signal received by the receiver. The transmitter includes: a light emitter capable of emitting optical signals of at least two flicker frequencies; and a hollow hemispherical cover provided with fixed-angle opaque sections and variable-angle opaque sections, and regions between the fixed-angle opaque sections and the variable-angle opaque sections being light transmission regions.

Abstract: In one embodiment, a computer-implemented method performed by a data processing (DP) accelerator, includes receiving, at the DP accelerator, first data representing a set of training data from a host processor; receiving, at the DP accelerator, a watermark kernel from the host processor; and executing the watermark kernel within the DP accelerator on an artificial intelligence (AI) model. The watermark kernel, when executed, is configured to: generate a new watermark by inheriting an existing watermark from a data object of the set of training data, train the AI model using the set of training data, and implant the new watermark within the AI model during training of the AI model. The DP accelerator then transmits second data representing the trained AI model having the new watermark implanted therein to the host processor.

Abstract: In one embodiment, a computer-implemented method performed by a data processing (DP) accelerator, the method includes receiving, at the DP accelerator, first data representing a set of training data from a host processor and performing training of an artificial intelligence (AI) model based on the set of training data within the DP accelerator. The method further includes implanting, by the DP accelerator, a watermark within the trained AI model and transmitting second data representing the trained AI model having the watermark implanted therein to the host processor. In an embodiment, the method further includes receiving a pre-trained machine learning model; and performing training for the pre-trained AI model based on the set of training data within the DP accelerator.

Abstract: In one embodiment, a computer-implemented method of a DP accelerator performing an encryption or decryption operation includes receiving, by the DP accelerator, a command and input data for the DP accelerator to encrypt or decrypt. The command is one of: encrypt the input data or decrypt the input data. The method further includes encrypting, or decrypting, by the DP accelerator, the input data according to the command; and providing the encrypted or decrypted input data to the host device. The host device and DP accelerator may exchange one or more keys and such keys can be used to establish a secure link between the host device and DP accelerator and/or to use for encryption or decryption. One or more of the keys may be based upon a root key or key pair of the DP accelerator and can be stored in a secure storage of a security unit of the DP accelerator.

Abstract: In one embodiment, a computer-implemented method performed by a data processing (DP) accelerator, includes receiving, at the DP accelerator, first data representing a set of training data from a host processor; receiving, at the DP accelerator, a watermark kernel from the host processor; and executing the watermark kernel within the DP accelerator on an artificial intelligence (AI) model. The watermark kernel, when executed, is configured to: generate a watermark, train the AI model using the set of training data, and implant the watermark within the AI model during training of the AI model. The DP accelerator then transmits second data representing the trained AI model having the watermark implanted therein to the host processor. In an embodiment, the method further includes receiving a pre-trained AI model and the training is performed for the pre-trained AI model.

Abstract: In one embodiment, a computer-implemented method of a data processing (DP) accelerator obtaining a watermark of an artificial intelligence (AI) model includes receiving, from a host device, the AI model to execute on the DP accelerator, and receiving input data that triggers output from the AI model on the DP accelerator. The DP accelerator calculates AI model output, in response to the received input and provides the output to the host device. The output can be a watermark extracted from the AI model. DP accelerator can call a security unit of the DP accelerator to digitally sign the output. In an embodiment, the security unit digitally signs the output from the AI model using a key that is retrieved from, or is derived from, a key stored in a secure storage on the security unit.

Abstract: In one embodiment, a computer-implemented method of a data processing (DP) accelerator encrypting or decrypting input data can include receiving, from a host device, a command, the input data, and a kernel. The kernel can be an encryption kernel, or a decryption kernel, and the DP accelerator need not know which kernel it has received. The DP accelerator runs the received kernel. In response to the DP accelerator receiving the command, the DP accelerator performs encrypting of the input data using the kernel, if the received kernel is an encryption kernel, otherwise, decrypting the input data using the kernel. The encrypted, or decrypted, input data is then provided to the host device.

Abstract: In one embodiment, a computer implemented method of a data processing (DP) accelerator providing a watermark of an artificial intelligence (AI) model to a host device includes receiving, by the DP accelerator, from the host device, the AI model, and a watermark-enabled kernel to the DP accelerator. The DP accelerator further receives from the host device, first input data to the DP accelerator that, when the first input data is used as input to the watermark-enabled kernel, generates a watermark of the AI model. The watermark is provided to the host device. In an embodiment, the method further includes receiving a signature kernel from the host device and calling the signature kernel to digitally sign the watermark. In an embodiment, the method alternatively includes calling a digital signature routine in a secure unit of the DP accelerator to digitally sign the watermark.

Abstract: In one embodiment, a computer-implemented method performed by a data processing (DP) accelerator includes receiving, at the DP accelerator, first data representing an artificial intelligence (AI) model that has been previously trained from a host processor; receiving, at the DP accelerator, a request to implant a watermark in the AI model from the host processor; and implanting, by the DP accelerator, the watermark within the AI model. The DP accelerator then transmits second data representing the AI model having the watermark implanted therein to the host processor. In embodiment, the method further includes extracting, at the DP accelerator, a watermark algorithm identifier (ID) from the request to implant a watermark; and generating the watermark using a watermark algorithm identified by the watermark algorithm ID.

Abstract: In one embodiment, a computer-implemented method of digitally signing input by a data processing (DP) accelerator operation, and embedding the digitally signed input into an output, includes receiving, from a host device, a signature kernel specifying input to the signature kernel and executing the signature kernel to: extract a watermark from the input and obtain a hash for the watermark; generate output from the input; and embed the hash into the output. The DP accelerator provides the output to the host device. In an embodiment, the input includes an artificial intelligence (AI) model that is executed by the DP accelerator. The DP accelerator receives second input from the host, thereby producing an inference output from the AI model. The digitally signed watermark of the AI Model is embedded into the inference output and is provided to the host device.

Abstract: In one embodiment, a computer-implemented method performed by a data processing (DP) accelerator, includes receiving, at the DP accelerator, first data representing an artificial intelligence (AI) model that has been previously trained from a host processor and a set of input data; receiving, at the DP accelerator, a watermark kernel from the host processor; and executing the watermark kernel within the DP accelerator on the AI model. The watermark kernel, when executed, is configured to: perform inference operations of the artificial intelligence model based on the input data to generate output data, and implant the watermark within the output data. The DP accelerator then transmits the output data having the watermark implanted therein to the host processor.

Abstract: In one embodiment, a computer-implemented method of a data processing (DP) accelerator obtaining a watermark of a watermark-enable artificial intelligence (AI) model includes receiving, by the DP accelerator, input data to the DP accelerator that causes the watermark-enabled AI model to extract the watermark from the watermark-enabled AI model; and providing the watermark of the watermark-enabled AI model to the host device. The DP accelerator can receive the model from the host device. The DP accelerator can further receive a command to digitally sign the watermark and call a security unit of the DP accelerator to digitally sign the watermark.

Abstract: A gimbal includes a rotation assembly. The rotation assembly includes an axis arm, a lock device, and an electric motor configured to drive the axis arm with a first driving force to rotate to a pre-set position, such that the lock device locks the axis arm at the pre-set position, and when the axis arm is at the pre-set position, drive the axis arm with a second driving force to rotate, such that the lock device unlocks the axis arm.

Abstract: Embodiments of the present disclosure disclose a precoding configuration method, device, and system. The precoding configuration method includes: generating precoding configuration information, where the precoding configuration information is used to configure a width of a frequency band for same precoding; and sending the precoding configuration information. The embodiments of the present disclosure further provide a transmit end device, a receive end device, and the precoding configuration system. In the precoding configuration method provided in the embodiments of the present disclosure, the precoding configuration information is generated and sent, so that a receive end can determine, based on the precoding configuration information, the width of the frequency band for same precoding.

Abstract: Calibration of a hexapod structure includes measuring a head pose of the hexapod structure relative to a base of the hexapod structure. Calibration of the hexapod structure also includes for each strut, independently increasing a length of each strut by a predefined amount from an original length and repeating the measuring of the head pose relative to the base. Calibration of the hexapod structure further includes for each strut, independently decreasing a length of each strut by a predefined amount from the original length and repeating the measuring of the head pose relative to the base. Additionally, calibration of the hexapod structure includes moving each strut back to the original length, and estimating joint errors for each strut prior to calibrating of the hexapod structure.

Abstract: One or more wireless stations may operate to configure direct communication with neighboring mobile stations, e.g., direct communication between the wireless stations without utilizing an intermediate access point. A mechanism for wireless stations to learn preferred channels of neighboring wireless stations and to schedule channel sequences within a time period based on the learned channels may include advertisement of preferred channels and adaptation of channel sequences, based on the preferred channels, to maximize bandwidth utilization.

Abstract: An interface circuit in an electronic device (such as an access point) may receive a wake-up-radio (WUR)-setup request associated with a recipient electronic device, where the WUR-setup request specifies one or more proposed data criteria or wake-up criteria that indicate when a wake-up frame is to be transmitted to the recipient electronic device. In response, the electronic device may determine one or more data criteria (or wake-up criteria) for use based at least in part on the one or more proposed data criteria. Then, the interface circuit may provide a WUR-setup response intended for the recipient electronic device, where the WUR-setup response indicates acceptance of the one or more proposed data criteria for use as the one or more data criteria or a proposed modification of at least one of the one or more proposed data criteria.

Abstract: Some embodiments of this disclosure include apparatuses and methods for implementing block acknowledgment (BA) operations for multi-link wireless communication networks. For example some embodiments relate to an electronic device including a transceiver and one or more processors communicatively coupled to the transceiver. The one or more processors transmit, using the transceiver and to a second electronic device, a first set of one or more frames on a first link and a second set of one or more frames on a second link. The one or more processors receive, using the transceiver and from the second electronic device, a first block acknowledgment (BA) frame on the first link and a second BA frame on the second link. The one or more processors further determine, based on received first BA frame and the second BA frame, a failed or missing frame of the first set of one or more frames transmitted on the first link.

Abstract: A keyword confirmation method and apparatus are provided. A keyword confirmation method includes: obtaining first audio data, the first audio data being recognized as a keyword; obtaining a pronunciation similarity probability of a similar pronunciation unit corresponding to at least one fragment of the first audio data and second audio data; determining that multiple contiguous silence fragments exist in second audio data contiguous in time with the first audio data; utilizing the silence probability, as well as a pronunciation similarity probability corresponding to fragment(s) of the first audio data and/or a pronunciation similarity probability corresponding to fragment(s) of the second audio data, evaluating whether the second audio data is silence; and confirming the first audio data as an effective keyword.