An encoding device and a decoding device use linear and nonlinear codes for encoding and decoding system data for a storage device. The encoding device includes a linear encoder for encoding first data to generate encoded data and a nonlinear transformer for transforming the encoded data with second data to generate output data. The first data includes data on a physical address corresponding to a logical address. The second data includes the logical address and a timestamp value indicating a version of map data mapping between the logical address and the physical address.

Representative apparatus, method, and system embodiments are disclosed for a self-scheduling processor which also provides additional functionality. Representative embodiments include a self-scheduling processor, comprising: a processor core adapted to execute a received instruction; and a core control circuit adapted to automatically schedule an instruction for execution by the processor core in response to a received work descriptor data packet. In another embodiment, the core control circuit is also adapted to schedule a fiber create instruction for execution by the processor core, to reserve a predetermined amount of memory space in a thread control memory to store return arguments, and to generate one or more work descriptor data packets to another processor or hybrid threading fabric circuit for execution of a corresponding plurality of execution threads. Event processing, data path management, system calls, memory requests, and other new instructions are also disclosed.

The present disclosure generally relates to a method and device for simultaneously decoding data. Rather than sending data to be decoded to a single decoder, the data can be sent to multiple, available decoders so that the data can be decode in parallel. The data decoded from the first decoder that completes decoding of the data will be delivered to the host device. All remaining decoded data that was decoded in parallel will be discarded. The decoders operating simultaneously in parallel can operate using different parameters such as different calculation precision (power levels). By utilizing multiple decoders simultaneously in parallel, the full functionality of the data storage device's decoding capabilities are utilized without increasing latency.

One embodiment provides for a compute apparatus to perform machine learning operations, the compute apparatus comprising instruction decode logic to decode a single instruction including multiple operands into a single decoded instruction, the multiple operands including a first operand and a second operand, the first operand including vector of one-hot coded weights and the second operand including a vector of input data; and a general-purpose graphics compute unit including a first logic unit, the general-purpose graphics compute unit to execute the single decoded instruction, wherein to execute the single decoded instruction includes to perform multiple operations on the first set of operands and the second set of operands.

A method, computer program, and computer system for point cloud coding is provided. Data corresponding to a point cloud is received, and one or more geometric features are detected from among the data corresponding to the point cloud. A representation is determined for one or more of the detected geometric features, and the received data is encoded or decoded based on the determined representations whereby the point cloud is reconstructed based on the decoded data.

Examples of systems and method described herein provide for the processing of image codes (e.g., a binary embedding) at a memory die. Such images codes may generated by various endpoint computing devices, such as Internet of Things (IoT) computing devices, Such devices can generate a Hamming processing command, having an image code of the image, to compare that representation of the image to other images (e.g., in an image dataset) to identify a match or a set of neural network results. Advantageously, examples described herein may be used in neural networks to facilitate the processing of datasets, so as to increase the rate and amount of processing of such datasets. For example, comparisons of image codes can be performed on a memory die itself, like a memory die of a NAND memory device.

Very long instruction word (VLIW) instruction processing using a reduced-width processor is disclosed. In a particular embodiment, a VLIW processor includes a control circuit configured to receive a VLIW packet that includes a first number of instructions and to distribute the instructions to a second number of instruction execution paths. The first number is greater than the second number. The VLIW processor also includes physical registers configured to store results of executing the instructions and a register renaming circuit that is coupled to the control circuit.

The present disclosure is related to performing speculation in, for example, a memory device or a computing system that includes a memory device. Speculation can be used to identify data that is accessed together or to predict data that will be accessed with greater frequency. The identified data can be organized to improve efficiency in providing access to the data.

An apparatus and method for scalable qubit addressing. For example, one embodiment of a processor comprises: a decoder comprising quantum instruction decode circuitry to decode quantum instructions to generate quantum microoperations (uops) and non-quantum decode circuitry to decode non-quantum instructions to generate non-quantum uops; execution circuitry comprising: an address generation unit (AGU) to generate a system memory address responsive to execution of one or more of the non-quantum uops; and quantum index generation circuitry to generate quantum index values responsive to execution of one or more of the quantum uops, each quantum index value uniquely identifying a quantum bit (qubit) in a quantum processor; wherein to generate a first quantum index value for a first quantum uop, the quantum index generation circuitry is to read the first quantum index value from a first architectural register identified by the first quantum uop.

An asymmetrical processing system is provided. The processor has a vector unit comprised of one or more computational units coupled with a vector memory space and a scalar unit coupled with a data memory space and the vector memory space, the scalar unit accessing one or more memory locations within the vector memory space.