Abstract: Systems, apparatuses, and methods for implementing duplicated registers for access by initiators across multiple semiconductor dies are disclosed. A system includes multiple initiators on multiple semiconductor dies of a chiplet processor. One of the semiconductor dies is the master die, and this master die has copies of registers which can be accessed by the multiple initiators on the multiple semiconductor dies. When a given initiator on a given secondary die generates a register access, the register access is routed to the master die and a particular duplicate copy of the register maintained for the given secondary die. From the point of view of software, the multiple semiconductor dies appear as a single die, and the multiple initiators appear as a single initiator. Multiple types of registers can be maintained by the master die, with a flush register being one of the register types.

Abstract: Systems and methods for utilizing microphone array information for acoustic modeling are disclosed. Audio data may be received from a device having a microphone array configuration. Microphone configuration data may also be received that indicates the configuration of the microphone array. The microphone configuration data may be utilized as an input vector to an acoustic model, along with the audio data, to generate phoneme data. Additionally, the microphone configuration data may be utilized to train and/or generate acoustic models, select an acoustic model to perform speech recognition with, and/or to improve trigger sound detection.

Abstract: Systems, apparatuses, and methods for implementing duplicated registers for access by initiators across multiple semiconductor dies are disclosed. A system includes multiple initiators on multiple semiconductor dies of a chiplet processor. One of the semiconductor dies is the master die, and this master die has copies of registers which can be accessed by the multiple initiators on the multiple semiconductor dies. When a given initiator on a given secondary die generates a register access, the register access is routed to the master die and a particular duplicate copy of the register maintained for the given secondary die. From the point of view of software, the multiple semiconductor dies appear as a single die, and the multiple initiators appear as a single initiator. Multiple types of registers can be maintained by the master die, with a flush register being one of the register types.

Abstract: Techniques for speech processing using a deep neural network (DNN) based acoustic model front-end are described. A new modeling approach directly models multi-channel audio data received from a microphone array using a first model (e.g., multi-geometry/multi-channel DNN) that is trained using a plurality of microphone array geometries. Thus, the first model may receive a variable number of microphone channels, generate multiple outputs using multiple microphone array geometries, and select the best output as a first feature vector that may be used similarly to beamformed features generated by an acoustic beamformer. A second model (e.g., feature extraction DNN) processes the first feature vector and transforms it to a second feature vector having a lower dimensional representation. A third model (e.g., classification DNN) processes the second feature vector to perform acoustic unit classification and generate text data. The DNN front-end enables improved performance despite a reduction in microphones.

Abstract: Systems, apparatuses, and methods for implementing a centralized interrupt controller to aggregate interrupts generated across multiple semiconductor dies are disclosed. A system includes multiple interrupt sources on multiple semiconductor dies. A centralized interrupt controller on one of the semiconductor dies receives and aggregates interrupts from the multiple interrupt sources on the multiple semiconductor dies. This facilitates a single transmission point for forwarding the interrupts to the processor and operating system responsible for handling interrupts. Each interrupt source embeds an ID when conveying an interrupt to the interrupt controller. This allows the interrupt controller to differentiate between the interrupt sources and to identify which source generated a given interrupt. The interrupt controller conveys an indication of the source of the interrupt to the processor when forwarding the interrupt to the processor.

Abstract: Techniques for speech processing using a deep neural network (DNN) based acoustic model front-end are described. A new modeling approach directly models multi-channel audio data received from a microphone array using a first model (e.g., multi-geometry/multi-channel DNN) that includes a frequency aligned network (FAN) architecture. Thus, the first model may perform spatial filtering to generate a first feature vector by processing individual frequency bins separately, such that multiple frequency bins are not combined. The first feature vector may be used similarly to beamformed features generated by an acoustic beamformer. A second model (e.g., feature extraction DNN) processes the first feature vector and transforms it to a second feature vector having a lower dimensional representation. A third model (e.g., classification DNN) processes the second feature vector to perform acoustic unit classification and generate text data. The DNN front-end enables improved performance despite a reduction in microphones.

Abstract: Techniques for speech processing using a deep neural network (DNN) based acoustic model front-end are described. A new modeling approach directly models multi-channel audio data received from a microphone array using a first model (e.g., multi-channel DNN) that takes in raw signals and produces a first feature vector that may be used similarly to beamformed features generated by an acoustic beamformer. A second model (e.g., feature extraction DNN) processes the first feature vector and transforms it to a second feature vector having a lower dimensional representation. A third model (e.g., classification DNN) processes the second feature vector to perform acoustic unit classification and generate text data. These three models may be jointly optimized for speech processing (as opposed to individually optimized for signal enhancement), enabling improved performance despite a reduction in microphones and a reduction in bandwidth consumption during real-time processing.

Abstract: A novel bamboo tube fighting toy comprises a baseplate. The baseplate has an upward flange. The flange is machined with grooves on two sides. An upper housing plate is fixed above the baseplate, and the upper housing plate is provided with a first elongated slot and a second elongated slot. A movable rod penetrates through the groove and is mounted below the second elongated slot. A first sleeve, a rolling ball, a first spring and a handle are mounted at an outer end of the movable rod. A second sleeve, a collar, a second spring and a cross bar are mounted at an inner end of the movable rod. The second sleeve is connected to above the movable rod. The collar is provided at an upper part of the second sleeve. The second spring is supported between the collar and the movable rod. The cross bar is embedded at a lower part of the movable rod. A control rope is fixed on the cross bar, and is connected with a bamboo tube robot on the upper housing plate by penetrating through the second sleeve.

Abstract: An approach to wakeword detection uses an explicit representation of non-wakeword speech in the form of subword (e.g., phonetic monophone) units that do not necessarily occur in the wakeword and that broadly represent general speech. These subword units are arranged in a “background” model, which at runtime essentially competes with the wakeword model such that a wakeword is less likely to be declare as occurring when the input matches that background model well. An HMM may be used with the model to locate possible occurrences of the wakeword. Features are determined from portions of the input corresponding to subword units of the wakeword detected using the HMM. A secondary classifier is then used to process the features to yield a decision of whether the wakeword occurred.

Abstract: Techniques for speech processing using a deep neural network (DNN) based acoustic model front-end are described. A new modeling approach directly models multi-channel audio data received from a microphone array using a first model (e.g., multi-channel DNN) that takes in raw signals and produces a first feature vector that may be used similarly to beamformed features generated by an acoustic beamformer. A second model (e.g., feature extraction DNN) processes the first feature vector and transforms it to a second feature vector having a lower dimensional representation. A third model (e.g., classification DNN) processes the second feature vector to perform acoustic unit classification and generate text data. These three models may be jointly optimized for speech processing (as opposed to individually optimized for signal enhancement), enabling improved performance despite a reduction in microphones and a reduction in bandwidth consumption during real-time processing.

Abstract: Techniques for speech processing using a deep neural network (DNN) based acoustic model front-end are described. A new modeling approach directly models multi-channel audio data received from a microphone array using a first model (e.g., multi-channel DNN) that takes in raw signals and produces a first feature vector that may be used similarly to beamformed features generated by an acoustic beamformer. A second model (e.g., feature extraction DNN) processes the first feature vector and transforms it to a second feature vector having a lower dimensional representation. A third model (e.g., classification DNN) processes the second feature vector to perform acoustic unit classification and generate text data. These three models may be jointly optimized for speech processing (as opposed to individually optimized for signal enhancement), enabling improved performance despite a reduction in microphones and a reduction in bandwidth consumption during real-time processing.

Abstract: Systems and methods for utilizing microphone array information for acoustic modeling are disclosed. Audio data may be received from a device having a microphone array configuration. Microphone configuration data may also be received that indicates the configuration of the microphone array. The microphone configuration data may be utilized as an input vector to an acoustic model, along with the audio data, to generate phoneme data. Additionally, the microphone configuration data may be utilized to train and/or generate acoustic models, select an acoustic model to perform speech recognition with, and/or to improve trigger sound detection.

Abstract: The present invention involves a type of small-sized pool and PTC heater for liquid heating. The PTC heating component of PTC heater is composed of several heating cores made of PTC ceramic material, completely eliminating fire risk. Meanwhile, PTC heating component external is equipped with at least one layer of insulation and sealing component, which further includes several insulation layers and at least one sealing layer outside the insulation layer, effectively eliminating electricity leakage risk. Two layers of above mentioned insulation and sealing component could meet the insulation requirements specified in IEC standard and UL standard, so it could be used for applications with large power. In addition, the PTC heater has good water route sealing ability, so it could be used for liquid heating in small size pool. The small-sized pool is equipped with PTC heater with several layers of insulation and sealing for liquid heating, safe and reliable.

Abstract: The present invention involves a type of small-sized pool and PTC heater for liquid heating. The PTC heating component of PTC heater is composed of several heating cores made of PTC ceramic material, completely eliminating fire risk. Meanwhile, PTC heating component external is equipped with at least one layer of insulation and sealing component, which further includes several insulation layers and at least one sealing layer outside the insulation layer, effectively eliminating electricity leakage risk. Two layers of above mentioned insulation and sealing component could meet the insulation requirements specified in IEC standard and UL standard, so it could be used for applications with large power. In addition, the PTC heater has good water route sealing ability, so it could be used for liquid heating in small size pool. The small-sized pool is equipped with PTC heater with several layers of insulation and sealing for liquid heating, safe and reliable.