The present disclosure concerns a method to train, on a computing device, a machine-learning model adapted to determine a dysfunction of a monitored vehicle electronic control unit (ECU) or vehicle embedded computer. In aspects, the computing device stores, in a memory, historical data from a plurality of ECUs having a dysfunction. The historical data may include usage values over a period of time of at least one ECU resource by applications running on the ECUs. Further, the computing device may process the historical data to obtain two-dimensional training files. In implementations, each usage value may be linked with a specific application in a first dimension and a specific time in a second dimension. Still further, the computing device may train a machine-learning model with the training files.

An apparatus has processing circuitry, an instruction decoder, and capability registers, each capability register to store a capability comprising a pointer and constraint metadata for constraining valid use of the pointer/capability. In response to a capability-generating address calculating instruction specifying an offset value, a reference capability register is selected as one of a program counter capability register and a further capability register. A result capability is generated for which the pointer of the result capability indicates a window address identifying a selected window within an address space, the selected window being offset from a reference window by a number of windows determined based on the offset value of the capability-generating address calculating instruction. The reference window comprises the window comprising an address indicated by the pointer of the reference capability register.

An apparatus is provided comprising storage elements to store data blocks, where each data block has capability metadata associated therewith identifying whether the data block specifies a capability, at least one capability type being a bounded pointer. Processing circuitry is then arranged to be responsive to a bulk capability metadata operation identifying a plurality of the storage elements, to perform an operation on the capability metadata associated with each data block stored in the plurality of storage elements. Via a single specified operation, this hence enables query and/or modification operations to be performed on multiple items of capability metadata, hence providing more efficient access to such capability metadata.

An apparatus is provided comprising storage elements to store data blocks, where each data block has capability metadata associated therewith identifying whether the data block specifies a capability, at least one capability type being a bounded pointer. Processing circuitry is then arranged to be responsive to a bulk capability metadata operation identifying a plurality of the storage elements, to perform an operation on the capability metadata associated with each data block stored in the plurality of storage elements. Via a single specified operation, this hence enables query and/or modification operations to be performed on multiple items of capability metadata, hence providing more efficient access to such capability metadata.

Disclosed herein are vector index registers for storing or loading indexes of true and/or false results of comparison operations in vector processors. Each of the vector index registers store multiple addresses for accessing multiple positions in operand vectors.

A universal floating-point Instruction Set Architecture (ISA) compute engine implemented entirely in hardware. The ISA compute engine computes directly with human-readable decimal character sequence floating-point representation operands without first having to explicitly perform a conversion-to-binary-format process in software. A fully pipelined convertToBinaryFromDecimalCharacter hardware operator logic circuit converts one or more human-readable decimal character sequence floating-point representations to IEEE 754-2008 binary floating-point representations every clock cycle. Following computations by at least one hardware floating-point operator, a convertToDecimalCharacterFromBinary hardware conversion circuit converts the result back to a human-readable decimal character sequence floating-point representation.

Retention of events of an ordered event stream is disclosed. Expiration of events stored in a segment of an ordered event stream (OES) can be desirable. New events are added to a head of an OES segment, and pruning events from a tail of the OES segment can be valuable. Processing applications can register a processing scheme for a segment, e.g., at-least-once processing, exactly-once processing, etc., and can generate checkpoints indicating a degree of advancement in processing events of the segment. The ordered event stream can determine a cut point indicative of a progress point, that before which, events of an OES can be marked as ready for expiration. However, events that are marked for expiration can be retained to allow processing based on a checkpoint, e.g., expiration of the event can be refused until there is an assurance the event was read by the processing application.

The present disclosure relates to a data processing method and device, and related products. The product may include a control unit. The control unit may include an instruction caching unit, an instruction processing unit, and a storage queue unit. The instruction caching unit is configured to store a calculation instruction associated with an artificial neural network computation. The instruction processing unit may be configured to parse the calculation instruction to obtain a plurality of computation instructions. The storage queue unit may be configured to store an instruction queue, where the instruction queue may include a plurality of computation instructions or calculation instructions to be executed in a sequence of the queue. By adopting the above method, the present disclosure may improve a computation efficiency of the related products when performing a neural network model computation.

A device includes a circular buffer, which, in operation, is organized into a plurality of subsets of buffers, and control circuitry coupled to the circular buffer. The control circuitry, in operation, receives a memory load command to load a set of data into the circular buffer. The memory load command has an offset parameter indicating a data offset and a subset parameter indicating a subset of the plurality of subsets into which the circular buffer is organized. The control circuitry responds to the command by identifying a set of buffer addresses of the circular buffer based on a value of the offset parameter and a value of the subset parameter, and loading the set of data into the circular buffer using the identified set of buffer addresses.

A device includes a circular buffer, which, in operation, is organized into a plurality of subsets of buffers, and control circuitry coupled to the circular buffer. The control circuitry, in operation, receives a memory load command to load a set of data into the circular buffer. The memory load command has an offset parameter indicating a data offset and a subset parameter indicating a subset of the plurality of subsets into which the circular buffer is organized. The control circuitry responds to the command by identifying a set of buffer addresses of the circular buffer based on a value of the offset parameter and a value of the subset parameter, and loading the set of data into the circular buffer using the identified set of buffer addresses.