Abstract:
Embodiments described herein provide improved methods and systems for generating metadata for media objects at a computational engine (such as an artificial intelligence engine) within the storage edge controller, and for storing and using such metadata, in data processing systems.
Abstract:
Embodiments described herein provide improved methods and systems for generating metadata for media objects at a computational engine (such as an artificial intelligence engine) within the storage edge controller, and for storing and using such metadata, in data processing systems.
Abstract:
Systems and methods are provided for improved designs and performance for iterative decoder systems. In some embodiments, the iterative decoder may be decoupled from FIR samples through an FIR RAM, thus resulting in a less complex design and shorter processing times. In some embodiments, an intermediate memory may be used when passing information between the SOVA and LDPC of the iterative decoder. In some embodiments, the SOVA-required information may be continuously serialized from information received from the LDPC during each LDPC iteration. In some embodiments, the 1/(1+D2) precoder of the HR RLL encoder may be split into two serial, 1/(1+D) precoders. One 1/(1+D) precoder may be pulled outside of the HR RLL encoder and used in conjunction with the iterative decoder. This may allow for a 1/(1+D) precoder that may be used with the iterative decoder while maintaining the RLL constraints imposed upon the encoded information by the HR RLL encoder.
Abstract:
This disclosure describes a storage aggregator controller with metadata computation control, The storage aggregator controller communicates, via a host interface, over a computer network with one or more remote hosts, and also communicates, via a storage device interface, with a plurality of local storage devices, which are separate from the remote host(s) and which have respective non-volatile memories. The storage aggregator controller manages the local storage devices for storage or retrieval of media objects. The storage aggregator controller also governs a selective computation, at aggregator control circuitry or at a storage device controller of one or more of the storage devices, of metadata that defines content characteristics of the media objects that are retrieved from the plurality of storage devices or that are received from the one or more hosts over the computer network for storage in the plurality of storage devices.
Abstract:
A storage control device coupled to a storage device and located remotely from a host device receives media object data from the host device. The storage control device identifies a type of the media object data and select, based on the identified type, a computational model from among a plurality of computational models for use by a computational engine of the storage control device. The computational engine uses the selected computational model to generate metadata describing the media object data. The metadata is stored in the storage device so as to be selectively retrievable from the storage device separately from the media object data.
Abstract:
Systems and methods are provided for improved designs and performance for iterative decoder systems. In some embodiments, the iterative decoder may be decoupled from FIR samples through an FIR RAM, thus resulting in a less complex design and shorter processing times. In some embodiments, an intermediate memory may be used when passing information between the SOVA and LDPC of the iterative decoder. In some embodiments, the SOVA-required information may be continuously serialized from information received from the LDPC during each LDPC iteration. In some embodiments, the 1/(1+D 2 ) precoder of the HR RLL encoder may be split into two serial, 1/(1+D) precoders. One 1/(1+D) precoder may be pulled outside of the HR RLL encoder and used in conjunction with the iterative decoder. This may allow for a 1/(1+D) precoder that may be used with the iterative decoder while maintaining the RLL constraints imposed upon the encoded information by the HR RLL encoder.
Abstract:
Systems and methods are provided for bit flipping decoding with reliability inputs for LDPC codes. An LDPC decoder receives a variable node value and reliability data for a variable node, and check node values for check nodes associated with the variable node. Circuitry generates an updated variable node value, based on the received reliability data and the received check node values. The circuitry also generates, for at least one check node, an updated check node value based on the updated variable node value. An update rule may be based on whether checks of the variable node value are satisfied or unsatisfied, whether the variable node value has been previously updated, flipped, or toggled, based on the value of the reliability data, or a suitable combination thereof.
Abstract:
Systems, methods, and other embodiments associated with LDPC decoder architectures are described. According to one embodiment, an apparatus includes a super-parity-check matrix. The super matrix corresponds to at least a portion of a low density parity check (LDPC) code matrix. The super-parity-check matrix is coupled to a high throughput LDPC decoder and a low throughput LDPC decoder. The super-parity- check matrix includes n parity check matrices. The parity check matrices include x rows corresponding to x check node processing elements and y columns corresponding to y bit node processing elements. Thus, the super-parity-check matrix comprises nx rows corresponding to nx check node processing elements and ny columns corresponding to ny bit node processing elements. The numbers n, x, and y are selected so that ny codeword bits corresponding to the super-parity-check matrix can be processed in single time unit by the high throughput decoder and y codeword bits corresponding to the super-parity- check matrix can be processed in a single time unit by the low throughput decoder.
Abstract:
Improved decoding techniques for linear block codes, such as low-density parity-check (LDPC) codes (500). In one example, modifications to a conventional belief-propagation (BP) decoding algorithm (50A) for LDPC codes significantly improve the performance of the decoding algorithm so as to more closely approximate that of the theoretically optimal maximum-likelihood (ML) decoding scheme. BP decoder (50A) performance generally is improved for lower code block lengths, and significant error floor reduction or elimination may be achieved for higher code block lengths (82). In one aspect, significantly improved performance of a modified BP algorithm is achieved while at the same time essentially maintaining the benefits of relative computational simplicity and execution speed of a conventional BP algorithm as compared to an ML decoding scheme (51).
Abstract:
Various modifications to conventional information coding schemes that result in an improvement in one or more performance measures for a given coding scheme. Some examples are directed to improved decoding techniques for linear block codes, such as low-density parity-check (LDPC) codes. In one example, modifications to a conventional belief-propagation (BP) decoding algorithm for LDPC codes significantly improve the performance of the decoding algorithm so as to more closely approximate that of the theoretically optimal maximum-likelihood (ML) decoding scheme. BP decoder performance generally is improved for lower code block lengths, and significant error floor reduction or elimination may be achieved for higher code block lengths. In one aspect, significantly improved performance of a modified BP algorithm is achieved while at the same time essentially maintaining the benefits of relative computational simplicity and execution speed of a conventional BP algorithm as compared to an ML decoding scheme. In another aspect, modifications for improving the performance of conventional BP decoders are universally applicable to "off the shelf' LDPC encoder/decoder pairs. Furthermore, the concepts underlying the various methods and apparatus disclosed herein may be more generally applied to various decoding schemes involving iterative decoding algorithms and message-passing on graphs, as well as coding schemes other than LDPC codes to similarly improve their performance. Exemplary applications for improved coding schemes include wireless (mobile) networks, satellite communication systems, optical communication systems, and data recording and storage systems (e.g., CDs, DVDs, hard drives, etc.).