User-Behavior-Guided Dynamic Loaders in Embedded Operating Systems

This publication describes a new user-behavior-guided dynamic loader in embedded operating systems (OS). It is well-understood that embedded devices, such as smartphones, are constrained in their random-access memory (RAM) resource. To better utilize the RAM resource, developers use shared libraries when building their application software (often referred to as apps or applications). Shared libraries reduce the memory footprint of individual application software. However, the utilization of shared libraries, even though beneficial in minimizing the volatile memory footprint, comes at a performance cost. The performance is improved, however, when shared libraries are pre-loaded before the application software is accessed by the end user. Current OS platforms lack the heuristic guided approach to predict which shared libraries are going to be needed at the time the end user accesses the application software. To this end, a new dynamic loader is developed to help predict the pre-loading of the needed shared libraries. To enable the OS to predict and pre-load shared libraries tailored to the end user, the new user-behavior-guided dynamic loader employs three components: user embedding, current time, and current location. To improve the performance of the dynamic loader, federated learning is utilized to democratize the computational power needed and benefit from each end user’s input data. By so doing, the described techniques optimize the prediction of the relevant shared libraries to be pre-loaded, while protecting the end user’s privacy. Consequently, user-behavior-guided dynamic loaders reduce the memory pressure of the embedded devices, while optimizing the performance of these devices

Distributed Transfer Learning on Embedded Devices

An Internet-of-Things (IoT) platform that enables the retraining of machine learning models on embedded devices is described. The IoT platform utilizes transfer learning to retrain models in a cluster of IoT products connected to each-other in a local-area network (LAN), personal-area network (PAN), or wireless personal-area network (WPAN), to be reused for a similar purpose. Unlike current IoT platforms, the distributed transfer learning IoT platform does not need to utilize a centralized computing system, such as a cloud-computing server or a network server to perform model training, but rather execute this training in the cluster of IoT products. To reach this goal, in addition to transfer learning, the described IoT platform supports application programming interfaces (APIs) that specify a small portion of the existing pretrained model to be retrained, specify a data pipeline in the cluster of IoT devices to be used to retrain the model, and tune the model

PEER LEARNING ON THE EDGE IN VEHICLES

A vehicle head unit may train a surround-view (SV) detection module to rectify distortions in fish-eye camera images of the surroundings of a vehicle by comparing the object (e.g., traffic signs, lane markings, etc.) detection results of the SV detection module with those of an advanced driver assistance system (ADAS) detection module (e.g., while the SV detection module and the ADAS detection module are detecting the same objects of the same scenery). The vehicle head unit may receive the object detection results of the ADAS detection module by using one or more communication processes. For example, the vehicle head unit may use the object detection results of the ADAS detection module as ground truth data for training the SV detection module. The vehicle head unit may then update parameters, weights, and/or the like of the SV detection module to decrease the difference between the object detection results of the SV detection module and ADAS detection module. In some examples, the vehicle head unit may send (potentially after anonymizing personally identifiable information) the updated parameters, weights, and/or the like of the SV detection module to a remote computing system (e.g., a cloud server) to train a machine learning model that implements SV detection modules. The machine learning model may be trained using the collective updated parameters, weights, and/or the like of multiple SV detection modules

Transfer Inference Learning

Techniques are described that combine machine learning with an edge network that includes IoT devices to yield an effective and efficient method of assessing a condition of an environment. An inference module that includes a machine-learning algorithm, installed and executing on the IoT devices, assesses a condition detected from multiple, different geographic locations. The IoT devices transfer sets of data and inferences as well as respective sets of confidence levels to converge on a verified set of inferences. The verified set of inferences is arrived at quickly and with a high confidence level