Abstract: A vehicle seat component includes a mounting structure, and a cover member attachable to the mounting structure. When the cover member is attached to the mounting structure, the cover member is movable between a first position for at least partially covering an attachment location configured for attaching an additional device, and a second position for exposing the attachment location. The mounting structure and the cover member are configured so that the cover member is attachable to the mounting structure by moving the cover member relative to the mounting structure in a mounting direction when the cover member is in a mounting orientation relative to the mounting structure. When the cover member is attached to the mounting structure, the cover member is inhibited from detachment from the mounting structure when the cover member is in the first position or the second position.

Abstract: A microphone device includes a number N of at least two serially coupled microphones forming a microphone chain. The microphones are configured to transmit data to a controller via the microphone chain. The microphone chain is configured to output time-multiplexed data transmitted by the microphones.

Abstract: A pressure sensor device includes a semiconductor die having a die surface that includes a pressure sensitive area; and a bond wire bonded to a first peripheral region of the die surface and extends over the die surface to a second peripheral region of the die surface, wherein the pressure sensitive area is interposed between the second peripheral region and the first peripheral region, wherein the bond wire comprises a crossing portion that overlaps an area of the die surface, and wherein the crossing portion extends over the pressure sensitive area that is interposed between the first and the second peripheral regions.

Abstract: A method includes providing a first data channel configured for a transmission of acoustic data between a first microphone and a controller, providing a second data channel configured for a transmission of acoustic data between a second microphone and the controller, and transmitting non-acoustic data generated by the first microphone via the second data channel.

Abstract: Examples provide for an apparatus, method, and computer program for comparing the output of sensor cells in an arrangement of sensor cells in an area A, including a set of at least two measurement units. A measurement unit includes at least two sensor cells, wherein at least one sensor cell of at least one measurement unit includes a sensitive sensor cell, which is sensitive with respect to a measured quantity. The sensor cells are intermixed with each other. The apparatus further includes means for selecting output signals of sensor cells of the arrangement and means for determining a measured quantity or determining an intact sensor cell by comparing output signals of different measurement units.

Abstract: A microphone device includes a number N of at least two serially coupled microphones forming a microphone chain. The microphones are configured to transmit data to a controller via the microphone chain. The microphone chain is configured to output time-multiplexed data transmitted by the microphones.

Abstract: A method includes providing a first data channel configured for a transmission of acoustic data between a first microphone and a controller, providing a second data channel configured for a transmission of acoustic data between a second microphone and the controller, and transmitting non-acoustic data generated by the first microphone via the second data channel.

Abstract: A pressure sensor module including a housing, a pressure sensor chip, and one or more of an integrated passive device (IDP) chip and discrete passive devices are disclosed. The pressure sensor chip and one or more of the IPD chip and the discrete passive devices are arranged within the housing.

Abstract: A pressure sensor device includes a semiconductor die having a die surface that includes a pressure sensitive area; and a bond wire bonded to a first peripheral region of the die surface and extends over the die surface to a second peripheral region of the die surface, wherein the pressure sensitive area is interposed between the second peripheral region and the first peripheral region, wherein the bond wire comprises a crossing portion that overlaps an area of the die surface, and wherein the crossing portion extends over the pressure sensitive area that is interposed between the first and the second peripheral regions.

Abstract: A pressure sensor device includes a semiconductor die of the pressure sensor device and a bond wire of the pressure sensor device. A maximal vertical distance between a part of the bond wire and the semiconductor die is larger than a minimal vertical distance between the semiconductor die and a surface of a gel covering the semiconductor die.

Abstract: An electrical component includes a conductor having a plurality of conductor sections. The conductor sections are electrically short-circuited. The short circuit is at least partially eliminated when the temperature of the component exceeds a threshold or limit value. An electrical circuit including the component and a method for increasing the inductance of an electrical component having a conductor, are also provided.

Abstract: A pressure sensor module including a housing, a pressure sensor chip, and one or more of an integrated passive device (IDP) chip and discrete passive devices are disclosed. The pressure sensor chip and one or more of the IPD chip and the discrete passive devices are arranged within the housing.

Abstract: A pressure sensor module including a housing, a pressure sensor chip, and one or more of an integrated passive device (IDP) chip and discrete passive devices are disclosed. The pressure sensor chip and one or more of the IPD chip and the discrete passive devices are arranged within the housing.

Abstract: The embodiments described herein relate to recompiling an execution plan of a machine-learning program during runtime. An execution plan of a machine-learning program is compiled. In response to identifying a directed acyclic graph of high-level operations (HOP DAG) for recompilation during runtime, the execution plan is dynamically recompiled. The dynamic recompilation includes updating statistics and dynamically rewriting one or more operators of the identified HOP DAG, recomputing memory estimates of operators of the rewritten HOP DAG based on the updated statistics and rewritten operators, constructing a directed acyclic graph of low-level operations (LOP DAG) corresponding to the rewritten HOP DAG based in part on the recomputed memory estimates, and generating runtime instructions based on the LOP DAG.

Abstract: Examples provide for an apparatus, method, and computer program for comparing the output of sensor cells in an arrangement of sensor cells in an area A, including a set of at least two measurement units. A measurement unit includes at least two sensor cells, wherein at least one sensor cell of at least one measurement unit includes a sensitive sensor cell, which is sensitive with respect to a measured quantity. The sensor cells are intermixed with each other. The apparatus further includes means for selecting output signals of sensor cells of the arrangement and means for determining a measured quantity or determining an intact sensor cell by comparing output signals of different measurement units.

Abstract: A method for global data flow optimization for machine learning (ML) programs. The method includes receiving, by a storage device, an initial plan for an ML program. A processor builds a nested global data flow graph representation using the initial plan. Operator directed acyclic graphs (DAGs) are connected using crossblock operators according to inter-block data dependencies. The initial plan for the ML program is re-written resulting in an optimized plan for the ML program with respect to its global data flow properties. The re-writing includes re-writes of: configuration dataflow properties, operator selection and structural changes.

Abstract: A pressure sensor device includes a semiconductor die of the pressure sensor device and a bond wire of the pressure sensor device. A maximal vertical distance between a part of the bond wire and the semiconductor die is larger than a minimal vertical distance between the semiconductor die and a surface of a gel covering the semiconductor die.

Abstract: Parallel execution of machine learning programs is provided. Program code is received. The program code contains at least one parallel for statement having a plurality of iterations. A parallel execution plan is determined for the program code. According to the parallel execution plan, the plurality of iterations is partitioned into a plurality of tasks. Each task comprises at least one iteration. The iterations of each task are independent. Data required by the plurality of tasks is determined. An access pattern by the plurality of tasks of the data is determined. The data is partitioned based on the access pattern.

Abstract: A method for optimization of machine learning (ML) workloads on a graphics processor unit (GPU). The method includes identifying a computation having a generic pattern commonly observed in ML processes. Hierarchical aggregation spanning a memory hierarchy of the GPU for processing is performed for the identified computation including maintaining partial output vector results in shared memory of the GPU. Hierarchical aggregation for vectors is performed including performing intra-block aggregation for multiple thread blocks of a partial output vector results on GPU global memory.

Abstract: One embodiment provides a method for runtime piggybacking of concurrent data-parallel jobs in task-parallel machine learning (ML) programs including intercepting, by a processor, executable jobs including executable map reduce (MR) jobs and looped jobs in a job stream. The processor queues the executable jobs, and applies runtime piggybacking of multiple jobs by processing workers of different types. Runtime piggybacking for a ParFOR (parallel for) ML program is optimized including configuring the runtime piggybacking based on processing worker type, degree of parallelism and minimum time thresholds.