Abstract: A novel reverse osmosis or nanofiltration system (RO/NF) capable of detecting and responding to onset of fouling within the system utilizing uniquely configured membrane permeate flow path within the system which generates a time-sensitive data. Membrane performance data in real-time operating conditions is then utilized for rapid detection of membrane fouling, fouling rate, and cause of fouling, followed by controller-based system generated actions to stop, and recover from fouling or slow-down fouling, and, if required, to predict, plan, and schedule operator intervention steps to recover optimum system operating conditions. The end-result is a novel energy-efficient and fouling-managed advanced (machine learning) reverse osmosis system for brackish water desalination.

Abstract: Systems and methods for consolidating data sources for expediting validation processes are described herein. A user interface can be provided to a first entity. First risk assessment data associated with the first entity can be received via the user interface. Second risk assessment data associated with the first entity can be received. The first risk assessment data and the second risk assessment data can be validated. The first risk assessment data and the second risk assessment data can be output for display on the user interface. A responsive message including the validated first risk assessment data and the validated second risk assessment data can be transmitted to a remote computing device for use in controlling access of the first entity to one or more interactive computing environments.

Abstract: A hash accelerator of a cache memory may receive a query from a processor comprising the cache memory, the query to comprise an input key and an operation to be performed based on a hash table stored in the cache memory. The hash accelerator may determine whether an entry associated with the input key exists in a lock board of the hash accelerator. The hash accelerator may process the query based on whether the entry exists in the lock board.

Abstract: A DNN accelerator includes a multiplication controller controlling whether to perform matrix computation based on weight values. The multiplication controller reads a weight matrix from a WRAM in the DNN accelerator and determines a row value for a row in the weight matrix. In an embodiment where the row value is one, a first switch sends a read request to the WRAM to read weights in the row and a second switch forms a data transmission path from an IRAM in the DNN accelerator to a PE in the DNN accelerator. The PE receives the weights and input data stored in the IRAM and performs MAC operations. In an embodiment where the row value is zero, the first and second switches are not triggered. No read request is sent to the WRAM and the data transmission path is not formed. The PE will not perform any MAC operations.