Abstract: Described herein are systems and methods that may efficiently detect multi-return light signals. A light detection and ranging system, such as a LIDAR system, may fire a laser beam that may hit multiple objects with a different distance in one line, causing multi-return light signals to be received by the system. Multi-return detectors may be able to analyze the peak magnitude of a plurality of peaks in the return signals and determine a multitude of peaks, such as the first peak, the last peak and the maximum peak. One embodiment to detect the multi-return light signals may be a multi-return recursive matched filter detector. This detector comprises a matched filter, peak detector, centroid calculation and a zeroing out function. Other embodiments may be based on a maximum finder that algorithmically selects the highest magnitude peaks from samples of the return signal and buffers for regions of interests peaks.

Abstract: Described herein are systems and methods that that mitigate avalanche photodiode (APD) blinding and allow for improved accuracy in the detection of a multi-return light signal. A blinding spot may occur due to saturation of a primary APD. The systems and methods include the incorporation of a redundant APD and the utilization of time diversity and space diversity. Detection by the APDs is activated by a bias signal. The redundant APD receives a time delayed bias signal compared to the primary APD. Additionally, the redundant APD is positioned off the main focal plane in order to attenuate an output of the redundant APD. With attenuation, the redundant APD may not saturate and may have a successful detection during the blinding spot of the primary APD. Embodiments may include multiple primary APDs and multiple secondary APDs.

Abstract: Certain aspects of the present disclosure provide techniques for partitioning feature maps to improve machine learning model processing. In one aspect, a method, includes partitioning a feature map row-wise into a plurality of feature sub-maps such that: each respective feature sub-map of the plurality of feature sub-maps is defined with respect to a split row determined based on a dense data element count for each row of the feature map; and each feature sub-map of the plurality of feature sub-maps has a same column dimensionality as the feature map; and assigning each of the plurality of feature sub-maps to one of a plurality of tensor compute units and one of a plurality of tensor feature map memory units for processing in parallel.

Abstract: A method and system for decoding low density parity check (LDPC) codes. An LDPC code decoder includes LDPC decoding circuitry comprising a Q message generator and a P sum adder array. The Q message generator combines an R message from a previous iteration with a P message to produce a Q message. The P sum adder array adds the P message to a difference of an R message from a current iteration and the R message from the previous iteration to produce an updated P message.

Abstract: A method and system for decoding low density parity check (“LDPC”) codes. An LDPC code decoder includes LDPC decoding circuitry comprising a Q message generator and a P sum adder array. The Q message generator combines an R message from a previous iteration with a P message to produce a Q message. The P sum adder array adds the P message to a difference of an R message from a current iteration and the R message from the previous iteration to produce an updated P message.

Abstract: Described herein are systems and methods that may efficiently detect multi-return light signals. A light detection and ranging system, such as a LIDAR system, may fire a laser beam that may hit multiple objects with a different distance in one line, causing multi-return light signals to be received by the system. Multi-return detectors may be able to analyze the peak magnitude of a plurality of peaks in the return signals and determine a multitude of peaks, such as the first peak, the last peak and the maximum peak. One embodiment to detect the multi-return light signals may be a multi-return recursive matched filter detector. This detector comprises a matched filter, peak detector, centroid calculation and a zeroing out function. Other embodiments may be based on a maximum finder that algorithmically selects the highest magnitude peaks from samples of the return signal and buffers for regions of interests peaks.

Abstract: A method, circuit, and system for managing wear levelling in non-volatile memory. First, an original physical block address (PBA) for a logical block address (LBA) of a write operation may be received. The original PBA is one of a set of PBAs for data blocks of a non-volatile memory array. Each of these PBAs may be uniquely mapped to a particular LBA using a multistage interconnection network (MIN). A swap PBA may next be determined for the LBA. The swap PBA may be selected from the set of PBAs uniquely mapped using the MIN. Then, the MIN may be configured to map the LBA to the swap PBA. Finally, data of a first data block stored at the original PBA may be swapped with data of a second data block stored at the swap PBA.

Abstract: A method and system for decoding low density parity check (“LDPC”) codes. An LDPC code decoder includes LDPC decoding circuitry comprising a Q message generator and a P sum adder array. The Q message generator combines an R message from a previous iteration with a P message to produce a Q message. The P sum adder array adds the P message to a difference of an R message from a current iteration and the R message from the previous iteration to produce an updated P message.

Abstract: A method and system for decoding low density parity check (“LDPC”) codes. An LDPC code decoder includes LDPC decoding circuitry comprising a Q message generator and a P sum adder array. The Q message generator combines an R message from a previous iteration with a P message to produce a Q message. The P sum adder array adds the P message to a difference of an R message from a current iteration and the R message from the previous iteration to produce an updated P message.

Abstract: A method, circuit, and system for managing wear levelling in non-volatile memory. First, an original physical block address (PBA) for a logical block address (LBA) of a write operation may be received. The original PBA is one of a set of PBAs for data blocks of a non-volatile memory array. Each of these PBAs may be uniquely mapped to a particular LBA using a multistage interconnection network (MIN). A swap PBA may next be determined for the LBA. The swap PBA may be selected from the set of PBAs uniquely mapped using the MIN. Then, the MIN may be configured to map the LBA to the swap PBA. Finally, data of a first data block stored at the original PBA may be swapped with data of a second data block stored at the swap PBA.

Abstract: Described herein are systems and methods that may efficiently detect multi-return light signals. A light detection and ranging system, such as a LiDAR system, may fire a laser beam that may hit multiple objects with a different distance in one line, causing multi-return light signals to be received by the system. Multi-return detectors may be able to analyze the peak magnitude of a plurality of peaks in the return signals and determine a multitude of peaks, such as the first peak, the last peak and the maximum peak. One embodiment to detect the multi-return light signals may be a multi-return recursive matched filter detector. This detector comprises a matched filter, peak detector, centroid calculation and a zeroing out function. Other embodiments may be based on a maximum finder that algorithmically selects the highest magnitude peaks from samples of the return signal and buffers for regions of interests peaks.

Abstract: Described herein are systems and methods that that mitigate avalanche photodiode (APD) blinding and allow for improved accuracy in the detection of a multi-return light signal. A blinding spot may occur due to saturation of a primary APD. The systems and methods include the incorporation of a redundant APD and the utilization of time diversity and space diversity. Detection by the APDs is activated by a bias signal. The redundant APD receives a time delayed bias signal compared to the primary APD. Additionally, the redundant APD is positioned off the main focal plane in order to attenuate an output of the redundant APD. With attenuation, the redundant APD may not saturate and may have a successful detection during the blinding spot of the primary APD. Embodiments may include multiple primary APDs and multiple secondary APDs.

Abstract: A method and system for decoding low density parity check (“LDPC”) codes. An LDPC code decoder includes LDPC decoding circuitry comprising a Q message generator and a P sum adder array. The Q message generator combines an R message from a previous iteration with a P message to produce a Q message. The P sum adder array adds the P message to a difference of an R message from a current iteration and the R message from the previous iteration to produce an updated P message.

Abstract: A method and system for decoding low density parity check (“LDPC”) codes. An LDPC code decoder includes LDPC decoding circuitry comprising a Q message generator and a P sum adder array. The Q message generator combines an R message from a previous iteration with a P message to produce a Q message. The P sum adder array adds the P message to a difference of an R message from a current iteration and the R message from the previous iteration to produce an updated P message.

Abstract: Described herein are systems and methods that may efficiently detect multi-return light signals. A light detection and ranging system, such as a LiDAR system, may fire a laser beam that may hit multiple objects with a different distance in one line, causing multi-return light signals to be received by the system. Multi-return detectors may be able to analyze the peak magnitude of a plurality of peaks in the return signals and determine a multitude of peaks, such as the first peak, the last peak and the maximum peak. One embodiment to detect the multi-return light signals may be a multi-return recursive matched filter detector. This detector comprises a matched filter, peak detector, centroid calculation and a zeroing out function. Other embodiments may be based on a maximum finder that algorithmically selects the highest magnitude peaks from samples of the return signal and buffers for regions of interests peaks.

Abstract: Described herein are systems and methods that that mitigate avalanche photodiode (APD) blinding and allow for improved accuracy in the detection of a multi-return light signal. A blinding spot may occur due to saturation of a primary APD. The systems and methods include the incorporation of a redundant APD and the utilization of time diversity and space diversity. Detection by the APDs is activated by a bias signal. The redundant APD receives a time delayed bias signal compared to the primary APD. Additionally, the redundant APD is positioned off the main focal plane in order to attenuate an output of the redundant APD. With attenuation, the redundant APD may not saturate and may have a successful detection during the blinding spot of the primary APD. Embodiments may include multiple primary APDs and multiple secondary APDs.

Abstract: Systems and methods for offloading processing from a host to one or more storage processing units using an interconnect network are provided. One such method includes receiving a processing task from the host at a first storage processing unit (SPU) of a plurality of SPUs via a host interface, performing, at the first SPU, the processing task, and transferring data from the first SPU to a second SPU via an interconnection network, where each of the plurality of SPUs includes a non-volatile memory (NVM) and a processing circuitry configured to perform the processing task.

Abstract: Described herein are systems and methods that may efficiently detect multi-return light signals. A light detection and ranging system, such as a LIDAR system, may fire a laser beam that may hit multiple objects with a different distance in one line, causing multi-return light signals to be received by the system. Multi-return detectors may be able to analyze the peak magnitude of a plurality of peaks in the return signals and determine a multitude of peaks, such as the first peak, the last peak and the maximum peak. One embodiment to detect the multi-return light signals may be a multi-return recursive matched filter detector. This detector comprises a matched filter, peak detector, centroid calculation and a zeroing out function. Other embodiments may be based on a maximum finder that algorithmically selects the highest magnitude peaks from samples of the return signal and buffers for regions of interests peaks.

Abstract: A method and system for decoding low density parity check (“LDPC”) codes. A method and system for decoding low density parity check (“LDPC”) codes. An LDPC code decoder includes decoding circuitry configured to process blocks of an LDPC matrix. The decoding circuitry includes a control unit that controls processing by the decoding circuitry. The control unit is configured to cause the decoding circuitry to process blocks of a layer of the LDPC matrix out of order.

Abstract: Described herein are systems and methods for improving detection of a return signal in a light ranging and detection system (LiDAR). The method includes the following steps at the LiDAR system: encoding and transmitting a sequence of pulses based on a user signature. Then, receiving a multi-return signal based on a reflection off objects of the sequences of pulses. The multi-return signal may be decoded based on the user signature, and then authenticated the via a correlation calculation. The user signature may determine an amplitude of a first pulse in the sequence of pulses, an amplitude of a second pulse of the sequence of pulses, and an interval between the first pulse and the second pulse. A bit representation of the user signature is orthogonal to a bit representation of another user signature of another LiDAR system. The user signature may be dynamically adjusted by the LiDAR system.