Systems, methods, apparatuses, and computer program products for error probability feedback are provided. One method may include transmitting, to at least one user equipment, a configuration for protocol data unit error probability calculation and reporting. The method may also include receiving, from the at least one user equipment, feedback related to the protocol data unit error probability.

Systems, methods, apparatuses, and computer program products for error probability feedback are provided. One method may include transmitting, to at least one user equipment, a configuration for protocol data unit error probability calculation and reporting. The method may also include receiving, from the at least one user equipment, feedback related to the protocol data unit error probability.

Hard errors are determined for an unsuccessful decoding of codeword bits read from NAND memory cells via a read channel and input to a low-density parity check (LDPC) decoder. A bit error rate (BER) for the hard errors is estimated and BER for the read channel is estimated. Hard error regions are found using a single level cell (SLC) reading of the NAND memory cells. A log likelihood ratio (LLR) mapping of the codeword bits input to the LDPC decoder is changed based on the hard error regions, the hard error BER, and/or the read channel BER.

A system for a fiber-optic network includes a transceiver. The transceiver includes a fiber-optic interface unit and a host unit. The host unit includes a low-complexity error correction decoder and a high-complexity error correction decoder. One or both from the low-complexity error correction decoder and the high-complexity error correction decoder are selected to decode input data from the fiber-optic interface unit, the input data including codewords.

Methods and apparatus are disclosed for managing the storage of static and dynamic neural network data within a non-volatile memory (NVM) die for use with deep neural networks (DNN). Some aspects relate to separate trim sets for separately configuring a static data NVM array for static input data and a dynamic data NVM array for dynamic synaptic weight data. For example, the static data NVM array may be configured via one trim set for data retention, whereas the dynamic data NVM array may be configured via another trim set for write performance. The trim sets may specify different configurations for error correction coding, write verification, and read threshold calibration, as well as different read/write voltage thresholds. In some examples, neural network regularization is provided within a DNN by setting trim parameters to encourage bit flips to avoid overfitting. Some examples relate to managing non-DNN data, such as stochastic gradient data.

Examples described herein utilize multi-layer neural networks, such as multi-layer recurrent neural networks to estimate message probability compute data based on encoded data (e.g., data encoded using one or more encoding techniques). The neural networks and/or recurrent neural networks may have nonlinear mapping and distributed processing capabilities which may be advantageous in many systems employing a neural network or recurrent neural network to estimate message probability compute data for a message probability compute (MPC) decoder. In this manner, neural networks or recurrent neural networks described herein may be used to implement aspects of error correction coding (ECC) decoders, e.g., an MPC decoder that iteratively decodes encoded data.

Examples described herein utilize multi-layer neural networks, such as multi-layer recurrent neural networks to estimate message probability compute data based on encoded data (e.g., data encoded using one or more encoding techniques). The neural networks and/or recurrent neural networks may have nonlinear mapping and distributed processing capabilities which may be advantageous in many systems employing a neural network or recurrent neural network to estimate message probability compute data for a message probability compute (MPC) decoder. In this manner, neural networks or recurrent neural networks described herein may be used to implement aspects of error correction coding (ECC) decoders, e.g., an MPC decoder that iteratively decodes encoded data.

A memory controller includes, in one embodiment, a memory interface, a plurality of decoders, and a controller circuit. The memory interface is configured to interface with a memory having a plurality of wordlines. Each decoder of the plurality of decoders is configured to determine a bit error rate (BER). The controller circuit configured to generate a plurality of bit-error-rate estimation scan (BES) hypotheses for one wordline of the plurality of wordlines, divide the plurality of BES hypotheses among the plurality of decoders, receive BER results from the plurality of decoders based on the plurality of BES hypotheses, and adjust one or more read locations of the one wordline based on the BER results from the plurality of decoders.

An error rate measuring apparatus includes: an operation unit that sets a codeword length, an FEC symbol length, and an FEC symbol error threshold; error counting means for counting FEC symbol error and an uncorrectable codeword; a display unit that performs display by setting one zone of a display area as one FEC symbol length, matching a zone length of a horizontal axis of the display area with one codeword length, and performing line feed in codeword length units according to presence or absence of the FEC symbol error in FEC symbol length units based on a counting result; search means for searching for the uncorrectable codeword starting from the cursor on the identification display; and display control means for performing display control of the cursor at a position of a head error of the searched uncorrectable codeword.

Memory devices, systems including memory devices, and methods of operating memory devices are described, in which memory devices are configured to poison data based on an indication provided by a host device coupled with the memory devices. The indication may include which one or more bits to poison (invert) at which stages of performing write or read operations. In some embodiments, the memory device may invert one or more bits according to the indication and then correct one or more errors associated with inverting the one or more bit to verify its on-die ECC functionality. In some embodiments, the memory device may provide the host device with poisoned data including one or more bits inverted according to the indication such that the host device may test system-level ECC functionality using the poisoned data.