Apple Inc.

Innoviz Technologies Ltd.

Western Digital Technologies, Inc.

Sandisk Technologies Inc.

Julian Vlaiko

Amit Steinberg

Oren Buskila

Omer Keilaf

Ronen Eshel

Guy Zohar

Uri Perlmutter

Nir Osiroff

David Elooz

Oren Rosenzweig

Naftali Sommer

Ofir Shalvi

Dmitry Vaysman

Moshe Neerman

Yehuda Hahn

Amir Day

Yair Antman

Oren Golov

Ovadya Menadeva

Yair Schwartz

Sartaj Ajrawat

Eran Sharon

Shay Benisty

Idan Alrod

Avraham Poza Meir

Dotan Sokolov

Tien-Chien Kuo

Gil Semo

Barak Rotbard

Ori Isachar

Michael Shachar

Nimrod Hermesh

Smadar David Raly

Atai Levy

Pavel Berman

Alexander Bazarsky

Ariel Navon

Alon Marcu

Rotem Sela

Hadas Oshinsky

Grishma Shah

Ariel Maislos

Nir Perry

Etai Zaltsman

Miki Sapir

Shahar Levy

Ori Moshe Stern

Nissim Elmaleh

Hanan Borukhov

Yair Alpern

Siddhesh Darne

Yoseph Hassan

Aki Bleyer

Oren Segal

Oded Yeruhami

Hanoch Kremer

Uzi Doron

Venky Ramachandra

Roni Ankonina

Systems and methods use LIDAR technology. In one implementation, a LIDAR system includes at least one processor configured to: control activation of at least one light source for illuminating a field of view; receive from at least one sensor having a plurality of detection elements reflections signals indicative of light reflected from objects in the field of view; dynamically allocate a first subset of the plurality of detection elements to constitute a first pixel; dynamically allocate a second subset of the plurality of detection elements to constitute a second pixel; following processing of the first pixel and the second pixel, dynamically allocate a third subset of the plurality of detection elements to constitute a third pixel, the third subset overlapping with at least one of the first subset and the second subset, and differing from each of the first subset and the second subset; and following processing of the first pixel and the second pixel, dynamically allocate a fourth subset of the plurality of detection elements to constitute a fourth pixel, the fourth subset overlapping with at least one of the first subset, the second subset, and the third subset, and differing from each of the first subset, the second subset, and the third subset.

Varying detection sensitivity between detections in LIDAR systems

Aspects of a storage device including a memory and a controller are provided, which allow for error detection or data integrity checking during data transfer of write operations and read operations. The controller may be configured to generate data integrity information based on at least one data byte to be written to the memory, and to transfer the at least one data byte contemporaneously with the data integrity information on separate data paths to the memory. The controller may be configured to select between transferring data bus inversion information or the data integrity information based on whether a data integrity protection mode is active between the memory and the controller. The memory may be configured to receive the at least one data byte and the data integrity information from the controller, and detect whether an error exists in the at least one data byte based on the data integrity information.

Toggle mode interface for data integrity protection

Instead of incorporating a single interface towards the host for transferring data, utilizing a designated write-only storage logging device. The write-only storage logging device can accept sequential streams and automatically overwrite. The controller will read the log material in a secure manner using a different and separate physical connection than the one used for write. The storage device may have LBA ranges that work as write-only as well as other LBA ranges, which are normal (both reads and writes are enabled). Both options will allow for a traditional file system as well as sharing the storage, but will still protect the log areas that would be used for events that should not be read out.

Read-protected storage device with sequential logging

During operation of a data storage device, a controller of the data storage device may initiate read/write operations based on workloads provided by a host device. When initiating the read/write operations, power consumption and the data rate of the data storage device are generally high. Over time, the data rate corresponding to the workload decreases. Thus, the power consumption may be decreased to correspond with the decreased data rate. In order to maintain a high efficiency while decreasing an amount of power utilized, the controller may duty cycle the data storage device to operate between performance states to maintain a high data rate while decreasing power consumption.

Highly efficient SSD power management through PCIe data rate modulation

Aspects of a storage device are provided that apply advanced thermal throttling with multi-tier extreme thermal throttling. Initially, a controller determines whether a first temperature measurement indicates that a temperature of the memory meets a first thermal threshold associated with a first-tier extreme thermal throttling or a second thermal threshold associated with a second-tier extreme thermal throttling. Subsequently, the controller enables the first-tier extreme thermal throttling when the temperature measurement indicates that the temperature of the memory meets the first thermal threshold, or the controller enables the second-tier extreme thermal throttling when the temperature measurement indicates that the temperature of the memory meets the second thermal threshold. The controller then determines whether a second temperature measurement indicates that the temperature of the memory has decreased to avoid thermal shutdown of the storage device. Storage device performance is thus improved through advanced thermal throttling without compromising data integrity.

Solid-state device with multi-tier extreme thermal throttling

Methods and apparatus for precise power cycle management in data storage devices are provided. One such apparatus is a data storage device that includes a non-volatile memory (NVM) and a processor coupled to the NVM. In such case, the processor is configured to determine a first peak power for a first power phase, operate the DSD at a first DSD power consumption that is less than the first peak power for the first power phase, determine a second peak power for a second power phase based on a residual power equal to a difference between a preselected average power threshold and the first DSD power consumption, and operate the DSD at a second DSD power consumption that is less than the second peak power for the second power phase.

Precise power cycle management in data storage devices

A system includes at least one processor configured to detect, based on point cloud information, portions of a particular object, and determine, based on the detected portions, at least a first portion having a first reflectivity corresponding to a license plate, and at least two additional spaced-apart portions corresponding to locations on the particular object other than a location of the first portion. The at least two additional portions have reflectivity substantially lower than the first reflectivity. The at least one processor is further configured to classify the particular object as a vehicle, based on a spatial relationship and a reflectivity relationship between the first portion and the at least two additional portions.

LIDAR systems and methods for detection and classification of objects

A vehicle-assistance system for classifying objects in a vehicle's surroundings. The system includes: at least one memory configured to store classification information for classifying a plurality of objects; and at least one processor configured to receive a plurality of detection results associated with light detection and ranging system (LIDAR) detection results, each detection result including location information, and further information indicative of at least two of the following detection characteristics: object surface reflectivity; temporal spreading of signal reflected from the object; object surface physical composition; ambient illumination measured at a LIDAR dead time; difference in detection information from a previous frame; and confidence level associated with another detection characteristic. The at least one processor is also configured to: access the classification information; and based on the classification information and the detection results, classify an object in the vehicle's surroundings.

Aspects of a storage device are provided which apply advanced thermal throttling in response to temperature changes based on multiple thermal power states for different types of cells, such as SLCs and MLCs. Initially, a controller determines that a temperature of the memory meets a thermal throttling threshold of a plurality of thermal throttling thresholds. Subsequently, the controller transitions into a thermal power state of a plurality of thermal power states when the temperature meets the thermal throttling threshold. The controller applies a thermal mitigation configuration associated with the thermal power state. The controller then determines that the temperature of the memory has reached a thermal equilibrium in the thermal power state based on the thermal mitigation configuration. Storage device performance is thus improved through advanced thermal throttling without compromising data integrity.

Solid-state device with multiple thermal power states

Aspects of a storage device are provided that apply history-based prediction modeling in advanced thermal throttling. Initially, a controller determines a temperature prediction based one or more thermal mitigation parameters using a history-based prediction model. Subsequently, the controller determines whether the temperature prediction indicates that an actual temperature of the memory is expected to meet a thermal throttling threshold of a plurality of thermal throttling thresholds. The controller then transitions into a thermal power state of a plurality of thermal power states when the temperature prediction indicates that the actual temperature of the memory is expected to meet the thermal throttling threshold. The controller applies a thermal mitigation configuration associated with the thermal power state and determines that the temperature of the memory has reached a thermal equilibrium based on the thermal mitigation configuration. Storage device performance is thus improved through history-based prediction modeling without compromising data integrity.

History-based prediction modeling of solid-state device temperature

A data storage device includes a memory device and a controller coupled to the memory device. The controller is configured to receive a command, such as from a host device, to write data to the memory device, perform toggle mode (TM) encoding on the data, and send the TM encoded data to the memory device. The memory device is configured to receive the TM encoded data, decode the TM encoded data, and write the decoded data to a location within the memory device. The memory device is further configured to receive a read command to read data from a location within the memory device, read the data, TM encode the data, and send the TM encoded data to the controller. The controller is configured to receive and decode the TM encoded data, and send the decoded data to a host device.

Toggle mode (&#x2122;) coding with circuit bounded array memory

A LIDAR system for use in a vehicle is provided. The LIDAR system may include at least one processor configured to control at least one light source for illuminating a field of view and scan a field of view by controlling movement of at least one deflector at which the at least one light source is directed. The at least one processor may also be configured to receive, from at least one sensor, reflections signals indicative of light reflected from an object in the field of view. The at least one processor may further be configured to detect at least one temporal distortion in the reflections signals, and determine from the at least one temporal distortion an angular orientation of at least a portion of the object.

Detecting angles of objects

The present disclosure provides systems and methods that use LIDAR technology. In one implementation, a LIDAR system includes at least one processor configured to: control activation of at least one light source for illuminating a field of view; receive from at least one sensor a reflection signal associated with an object in the field of view, a time lapse between light leaving the at least one light source and reflection impinging on the least one sensor constituting a time of flight; and alter an amplification parameter associated with the at least one sensor during the time of flight.

Dynamically allocating detection elements to pixels in LIDAR systems

Toggle mode (TM) coding with circuit bounded array memory

A vehicle-assistance system for classifying objects in a vehicle's surroundings is provided. The system may include at least one memory configured to store classification information for classifying a plurality of objects and at least one processor configured to receive, on a pixel-by-pixel basis, a plurality of measurements associated with LIDAR detection results. The measurements may include at least one of: a presence indication, a surface angle, object surface physical composition, and a reflectivity level. The at least one processor may also be configured to receive, on the pixel-by-pixel basis, at least one confidence level associated with each received measurement, and access the classification information. The at least one processor may further be configured to, based on the classification information and the received measurements with the at least one associated confidence level plurality, identify a of pixels as being associated with a particular object.

Classifying objects with additional measurements

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Kfar Saba, Israel

Julian Vlaiko