Abstract: The subject application relates to monitoring of drive by wire sensors in vehicles. Systems, methods and apparatuses of predictive maintenance of drive by wire systems of vehicles are provided. For example, a data storage device is configured in a vehicle to receive and store sensor data from at least one drive by wire system of the vehicle. During a period in which the vehicle is assumed to be operating normally, the sensor data stored in the data storage device is used to train an artificial neural network to recognize the normal patterns in the sensor data. Subsequently, using the trained artificial neural network, the data storage device determines whether operations of the at least one drive by wire system are abnormal based on the sensor data. A maintenance alert can be generated for the vehicle in response to a determination that the operations of the at least one drive by wire system are abnormal according to the artificial neural network.

Abstract: A dual-port, dual function memory device can be configured to perform operations on data written to the memory device using artificial intelligence (AI) circuitry, such as a neuromorphic array and/or a deep learning accelerator (DLA), of the memory device. The memory device can include a port dedicated for communication between the AI circuitry and a host device and another port dedicated for communication between a memory array of the memory device and a host device. Performing operations, such as image processing operations, using AI circuitry of a memory device can reduce data transfers, reduce resource consumption, and offload workloads from a host device.

Abstract: A solid state drive having a drive aggregator and a plurality of component solid state drive, including a first component solid state drive and a second component solid state drive. The drive aggregator has at least one host interface, and a plurality of drive interfaces connected to the plurality of component solid state drives. The drive aggregator is configured to generate, in the second solid state drive, a copy of a dataset that is stored in the first component solid state drive. In response to a failure of the first component solid state drive, the drive aggregator is configured to substitute a function of the first component solid state drive with respect to the dataset with a corresponding function of the second component solid state drive, based on the copy of the dataset generated in the second component solid state drive.

Abstract: One or more programming operations are performed to program initial host data at the memory sub-system. The initial host data is received from a host system. A set of usage parameter values associated with the initial host data programmed at the memory sub-system is determined in view of the one or more programming operations. A media management operation is scheduled to be performed between a first time period and a second time period. The first time period corresponds to a time period during which the memory sub-system is expected to receive a first set of subsequent host data. The second time period corresponds to another time period during which the memory sub-system is expected to receive a second set of subsequent host data.

Abstract: Systems, devices, and methods related to video surveillance as a service (VSaaS) are described. For example, a removable storage device, such as a secure digital (SD) memory card or a micro SD card, can be configured to run a virtual camera agent. When the removable storage device is inserted into a digital camera to provide a storage capacity for the digital camera, the agent can convert the video captured by the digital camera into video captured by a virtual camera. The virtual camera can be configured to be in compliance with the camera requirements of a VSaaS platform. Thus, a digital camera not in compliance with the platform can still be used with the platform through the deployment of the virtual camera that is enabled by the removable storage device.

Abstract: A system can include a memory device with an array of memory cells and a machine learning operation component. The machine learning operation component can perform a machine learning computation in association with the array of memory cells. The system can also include a processing device that is operatively coupled with the memory device to perform operations that include setting the memory device to a first mode based on a first mode setting signal received from a host system, where in the first mode, the processing device exposes the array of memory cells to the host system and routes input data from the host system to the array of memory cells. The operations can also include, setting the memory device to a second mode, where in the second mode, the processing device exposes the machine learning operation component to the host system.

Abstract: One or more parameters indicative of a request to segment the memory device into multiple partitions for use by a host system are received. Responsive to receiving the one or more parameters indicative of the request to segment the memory device into multiple partitions, a first partition is configured with one or more configuration settings based on the one or more parameters. Configuring the first partition includes determining a memory type from multiple memory types based on the one or more parameters, and configuring the first partition to operate as the determined memory type. The memory types define a number of bits that a memory cell of the first partition is to store.

Abstract: Systems, methods and apparatus of climate control in vehicle cabins where occupants of vehicles are located/seated. For example, a vehicle includes: a climate control system; and an artificial neural network configured to receive climate control input parameters and generate, based on the climate control input parameters as a function of time, predictions of setting adjustments for the climate control system. For example, the climate control input parameters can include at least one setting of the climate control system and at least one operating parameter of the vehicle; and the vehicle is configured to effectuate an adjustment of the climate control system based at least in part on the predictions generated by the artificial neural network.

Abstract: A solid state drive having a drive aggregator and multiple component solid state drives. The drive aggregator associates the host interfaces with different logical address spaces, interprets commands received from the host interfaces in the different logical address spaces, and implements the commands using the plurality of component solid state drives. Some of the host interfaces can be configured to share a common logical address space. Some of the logical address spaces can be configured to have an overlapping region that are hosted on the same set of memory units such that the memory units can be addressed in any of the logical address spaces having the overlapping region.

Abstract: A first deep learning accelerator (DLA) model can be executed using a first DLA chip of a DLA package. The first DLA chip can have a first computational capability and the first DLA model can have a first maximum accuracy value. Responsive to an accuracy value of first results from executing the first DLA model using the first DLA chip being less than a threshold accuracy value, signaling indicative of the first results can be provided directly to a second DLA chip of the DLA package and a second DLA model can be executed using the second DLA chip and the first results as inputs. The second DLA chip can have a second computational capability that is greater than the first computational capability. The second DLA model can have a second maximum accuracy value that is greater than the first maximum accuracy value.

Abstract: Apparatuses and methods can be related to encoding traffic between a host and a DLA. Traffic between a host can be encoded utilizing an autoencoder. Encoding traffic between a host and a DLA changes the bandwidth of the traffic. Changing the bandwidth of the traffic prevents the correlation between the bandwidth and the input from which the traffic is generated.

Abstract: A first deep learning accelerator (DLA) model can be executed using a first subset of a plurality of DLA cores of a DLA chip. A second DLA model can be executed using a second subset of the plurality of DLA cores of the DLA chip. The first subset can include a first quantity of the plurality of DLA cores. The second subset can include a second quantity of the plurality of DLA cores that is different than the first quantity of the plurality of DLA cores.

Abstract: Systems, devices, and methods related to a Deep Learning Accelerator and memory are described. For example, a removable media (e.g., a memory card, or a USB drive) may be configured to execute instructions with matrix operands and configured with: an interface to receive a video stream; and random access memory to buffer a portion of the video stream as an input to an Artificial Neural Network and to store instructions executable by the Deep Learning Accelerator and matrices of the Artificial Neural Network. Such a removable media can be used to replace an existing removable media used in a surveillance camera to record video or images. The Deep Learning Accelerator can execute the instructions to generate analytics of the buffer portion using the Artificial Neural Network, enabling the surveillance camera that is upgraded via the use of the removable media to provide intelligent services based on the analytics.

Abstract: An artificial neural network can be allocated to memory and operated. Performance of the artificial neural network can be periodically evaluated. The evaluation can include inputting a representative dataset to the artificial neural network and comparing an output of the artificial neural network to a known output for the representative dataset. The artificial neural network can be retrained at least partially in response to the evaluation yielding a sub-threshold result.

Abstract: An artificial neural network can be allocated to memory and operated. An error can occur in the memory and/or be detected in the memory. Layers of the artificial neural network can be remapped in the memory at least partially in response to the error. Performance of the artificial neural network can be evaluated before and/or after the remapping.

Abstract: A system includes a memory device with microbumps and a processing device. The processing device is operatively coupled with the memory device to perform operations. The operations include transmitting data for a machine learning operation based on a set of the microbumps of the memory device where the data is being stored at the memory device. In addition, the operations include determining a change in a condition of the machine learning operation. Furthermore, the operations include that, in response to determining the change in the condition of the machine learning operation, determining a new set of the microbumps of the memory device that are to transmit subsequent data for the machine learning operation. Moreover, the operations include transmitting the subsequent data using the new set of microbumps of the memory device.

Abstract: Systems, devices, and methods related to a Deep Learning Accelerator and memory are described. An integrated circuit may be configured with: a Central Processing Unit, a Deep Learning Accelerator configured to execute instructions with matrix operands; random access memory configured to store first instructions of an Artificial Neural Network executable by the Deep Learning Accelerator and second instructions of an application executable by the Central Processing Unit; one or connections among the random access memory, the Deep Learning Accelerator and the Central Processing Unit; and an input/output interface to an external peripheral bus. While the Deep Learning Accelerator is executing the first instructions to convert sensor data according to the Artificial Neural Network to inference results, the Central Processing Unit may execute the application that uses inference results from the Artificial Neural Network.

Abstract: A system includes a data storage device and a host computing device. The data storage device includes a host interface; integrated circuit memory cells; and a processing device. The processing device is configured to execute firmware to perform operations requested by commands received via the host interface and maintenance operations identified by the processing device independent of commands received via the host interface. The host computing device is coupled to the host interface to provide commands with addresses to access the integrated circuit memory cells according to the address. In response to a request, the host computing device is configured to reduce, to below a threshold, a rate of transmitting to the host interface commands to access integrated circuit memory cells; and power up the data storage device to cause the data storage device to perform the maintenance operations.

Abstract: Systems, methods and apparatus of vehicle security operations during parking. For example, a vehicle includes: a proximity sensor configured to detect presence of an object approaching the vehicle when the vehicle is in a parking state; at least one camera configured to monitor surroundings of the vehicle when the vehicle is in the parking state; and an artificial neural network configured to extract identification information of the object from images generated by the camera and determine a security classification of the presence of the object. The identification information is stored in the vehicle and/or transmitted to a server or a mobile device, in response to the security classification being in a predefined category.

Abstract: A first mode setting signal is received from a host system. The first mode setting signal indicates a first mode. A memory component is memory component to the first mode based on the first mode setting signal. In the first mode, memory cells of the memory component are exposed to the host system. A second mode setting signal is received from the host system. The second mode setting signal indicates a second mode. The memory component is set to the second mode based on the second mode setting signal. In the second mode, a machine learning operation component of the memory component is exposed to the host system.