Abstract: A speed planning method and apparatus and a calculating apparatus for automatic driving of a vehicle. The method comprises: using a training sample set to perform machine learning to obtain a machine learning model; partitioning an input space, and obtaining a decision result corresponding to a determined partition based on the obtained machine learning model to form a partition decision table of each partition corresponding to the corresponding decision result; and obtaining each dimensional feature vector of a vehicle while driving in real time as an input feature, determining an input partition to which the input feature belongs, and querying the partition decision table based on the determined partition to obtain the corresponding decision result.

Abstract: The present disclosure provides for a steering control method and a steering control system for self-driving of a vehicle. The method comprises the steps of: obtaining information about an expected steering angle of a vehicle based on an automatic planning control operation; detecting whether an effective torque is applied to a steering wheel by a driver; and when it is detected that the driver has applied the effective torque to the steering wheel, computing a difference between a turning angle of the steering wheel controlled by the driver and the expected steering angle of a vehicle, and determining a self-driving intent prompt torque according to the difference between the two, wherein the self-driving intent prompt torque is to be applied to a steering system.

Abstract: The present disclosure provides for a steering control method and a steering control system for self-driving of a vehicle. The method comprises the steps of: obtaining information about an expected steering angle of a vehicle based on an automatic planning control operation; detecting whether an effective torque is applied to a steering wheel by a driver; and when it is detected that the driver has applied the effective torque to the steering wheel, computing a difference between a turning angle of the steering wheel controlled by the driver and the expected steering angle of a vehicle, and determining a self-driving intent prompt torque according to the difference between the two, wherein the self-driving intent prompt torque is to be applied to a steering system.

Abstract: A speed planning method and apparatus and a calculating apparatus for automatic driving of a vehicle. The method comprises: using a training sample set to perform machine learning to obtain a machine learning model; partitioning an input space, and obtaining a decision result corresponding to a determined partition based on the obtained machine learning model to form a partition decision table of each partition corresponding to the corresponding decision result; and obtaining each dimensional feature vector of a vehicle while driving in real time as an input feature, determining an input partition to which the input feature belongs, and querying the partition decision table based on the determined partition to obtain the corresponding decision result.

Abstract: A processing device includes a processing core, coupled to a memory, to execute a task including a code segment identified as being monitored and a kernel recorder, coupled to the processing core via a core interface. The kernel recorder includes a first filter circuit to responsive to determining that the task being executed enters the code segment, set the kernel recorder to a first mode under which the kernel recorder is to record, in a first record, a plurality of memory addresses accessed by the code segment, and responsive to determining that the execution of the task exits the code segment, set the kernel recorder to a second mode under which the kernel recorder is to detect a write operation to a memory address recorded in the first record and record the memory address in a second record.

Abstract: A speed planning method and apparatus and a calculating apparatus for automatic driving of a vehicle. The method comprises: using a training sample set to perform machine learning to obtain a machine learning model (S110); partitioning an input space, and obtaining a decision result corresponding to a determined partition based on the obtained machine learning model to form a partition decision table of each partition corresponding to the corresponding decision result (S120); and obtaining each dimensional feature vector of a vehicle while driving in real time as an input feature, determining an input partition to which the input feature belongs, and querying the partition decision table based on the determined partition to obtain the corresponding decision result (S130).

Abstract: A steering control method and a steering control system for self-driving of a vehicle are disclosed. The method comprises obtaining information about an expected steering angle of a vehicle based on an automatic planning control operation; detecting whether an effective torque is applied to a steering wheel by a driver; and when it is detected that the driver has applied the effective torque to the steering wheel, computing a difference between a turning angle of the steering wheel controlled by the driver and the expected steering angle of a vehicle, and determining a self-driving intent prompt torque according to the difference between the two, wherein the self-driving intent prompt torque is to be applied to a steering system.

Abstract: Disclosed are a steering control method and a steering control system (100, 200, 300) for self-driving of a vehicle. The method comprises the steps of: obtaining information about an expected steering angle of a vehicle based on an automatic planning control operation (S410); detecting whether an effective torque is applied to a steering wheel by a driver (S420); and when it is detected that the driver has applied the effective torque to the steering wheel, computing a difference between a turning angle of the steering wheel controlled by the driver and the expected steering angle of a vehicle, and determining a self-driving intent prompt torque according to the difference between the two, wherein the self-driving intent prompt torque is to be applied to a steering system (S430).

Abstract: A speed planning method and apparatus and a calculating apparatus for automatic driving of a vehicle. The method comprises: using a training sample set to perform machine learning to obtain a machine learning model (S110); partitioning an input space, and obtaining a decision result corresponding to a determined partition based on the obtained machine learning model to form a partition decision table of each partition corresponding to the corresponding decision result (S120); and obtaining each dimensional feature vector of a vehicle while driving in real time as an input feature, determining an input partition to which the input feature belongs, and querying the partition decision table based on the determined partition to obtain the corresponding decision result (S130).

Abstract: Particular embodiments described herein can offer an electronic fabric for a processing system that includes a fabric adapter to couple to a first fabric associated with a first system and to couple to a second fabric associated with a second system. The fabric adapter is configured to pass bidirectional communications between the first system and the second system. The electronic fabric can further include an address translation agent configured to map a first physical address in a first address space of the first system to a second physical address in a second address space of the second system.

Abstract: A processing device includes a processing core, coupled to a memory, to execute a task including a code segment identified as being monitored and a kernel recorder, coupled to the processing core via a core interface. The kernel recorder includes a first filter circuit to responsive to determining that the task being executed enters the code segment, set the kernel recorder to a first mode under which the kernel recorder is to record, in a first record, a plurality of memory addresses accessed by the code segment, and responsive to determining that the execution of the task exits the code segment, set the kernel recorder to a second mode under which the kernel recorder is to detect a write operation to a memory address recorded in the first record and record the memory address in a second record.

Abstract: A computer system may comprise a computer platform and input-output devices. The computer platform may include a plurality of heterogeneous processors comprising a central processing unit (CPU) and a graphics processing unit (GPU) and a shared virtual memory supported by a physical private memory space of at least one heterogeneous processor or a physical shared memory shared by the heterogeneous processor. The CPU (producer) may create shared multi-version data and store such shared multi-version data in the physical private memory space or the physical shared memory. The GPU (consumer) may acquire or access the shared multi-version data.

Abstract: A processing device includes a first processor module comprising a first core designed according to a first instruction set (ISA), and a second processor module comprising a second core designed according to a second ISA. The first and second processor modules are fabricated on a same die.

Abstract: According to some embodiments, a communication module 120 may be configured to transmit data packet traffic and a management module 110 may be configured to shape the data packet traffic transmitted by the communication module 120. The management module 110 may shape the data packet traffic by buffering data packets routed at different times to the communication module 120 based on at least one power management factor.

Abstract: A computing platform may include heterogeneous processors (e.g., CPU and a GPU) to support sharing of virtual functions between such processors. In one embodiment, a CPU side vtable pointer used to access a shared object from the CPU 110 may be used to determine a GPU vtable if a GPU-side table exists. In another embodiment, a shared non-coherent region, which may not maintain data consistency, may be created within the shared virtual memory. The CPU and the GPU side data stored within the shared non-coherent region may have a same address as seen from the CPU and the GPU side. However, the contents of the CPU-side data may be different from that of GPU-side data as shared virtual memory may not maintain coherency during the run-time. In one embodiment, the vptr may be modified to point to the CPU vtable and GPU vtable stored in the shared virtual memory.

Abstract: A computing platform may include heterogeneous processors (e.g., CPU and a GPU) to support sharing of virtual functions between such processors. In one embodiment, a CPU side vtable pointer used to access a shared object from the CPU 110 may be used to determine a GPU vtable if a GPU-side table exists. In other embodiment, a shared non-coherent region, which may not maintain data consistency, may be created within the shared virtual memory. The CPU and the GPU side data stored within the shared non-coherent region may have a same address as seen from the CPU and the GPU side. However, the contents of the CPU-side data may be different from that of GPU-side data as shared virtual memory may not maintain coherency during the run-time. In one embodiment, the vptr may be modified to point to the CPU vtable and GPU vtable stored in the shared virtual memory.

Abstract: A computer system may comprise a computer platform and input-output devices. The computer platform may include a plurality of heterogeneous processors comprising a central processing unit (CPU) and a graphics processing unit (GPU) and a shared virtual memory supported by a physical private memory space of at least one heterogeneous processor or a physical shared memory shared by the heterogeneous processor. The CPU (producer) may create shared multi-version data and store such shared multi-version data in the physical private memory space or the physical shared memory. The GPU (consumer) may acquire or access the shared multi-version data.

Abstract: A computer system may comprise a computer platform and input-output devices. The computer platform may include a plurality of heterogeneous processors comprising a central processing unit (CPU) and a graphics processing unit (GPU) and a shared virtual memory supported by a physical private memory space of at least one heterogeneous processor or a physical shared memory shared by the heterogeneous processor. The CPU (producer) may create shared multi-version data and store such shared multi-version data in the physical private memory space or the physical shared memory. The GPU (consumer) may acquire or access the shared multi-version data.

Abstract: Particular embodiments described herein can offer an electronic fabric for a processing system that includes a fabric adapter to couple to a first fabric associated with a first system and to couple to a second fabric associated with a second system. The fabric adapter is configured to pass bidirectional communications between the first system and the second system. The electronic fabric can further include an address translation agent configured to map a first physical address in a first address space of the first system to a second physical address in a second address space of the second system.

Abstract: A computer system may comprise a computer platform and input-output devices. The computer platform may include a plurality of heterogeneous processors comprising a central processing unit (CPU) and a graphics processing unit) GPU, for example. The GPU may be coupled to a GPU compiler and a GPU linker/loader and the CPU may be coupled to a CPU compiler and a CPU linker/loader. The user may create a shared object in an object oriented language and the shared object may include virtual functions. The shared object may be fine grain partitioned between the heterogeneous processors. The GPU compiler may allocate the shared object to the CPU and may create a first and a second enabling path to allow the GPU to invoke virtual functions of the shared object. Thus, the shared object that may include virtual functions may be shared seamlessly between the CPU and the GPU.