Abstract:
Processor register protection management is disclosed. In embodiments, a method of processor register protection management can include determining a sensitive logical register for executable code generated by a compiler, generating an error-correction table identifying the sensitive logical register, and storing the error-correction table in a memory accessible by a processor. The processor can be configured to generate a duplicate register of the sensitive logical register identified by the error-correction table.
Abstract:
A mechanism is provided for an integrated circuit with power gating. A power header switch is configured to connect and disconnect any one of multiple circuits to a common voltage source, where a powered off circuit is disconnected from the common voltage source. A power-up sequencer includes an initial stages power-up component and a final stages power-up component. The final stages power-up component is configured to execute final stages of a power-up process for the powered off circuit, and the initial stages power-up component is configured to execute initial stages of the power-up process for the powered off circuit. The initial stages power-up component is activated in response to a predictive power-up request.
Abstract:
Systems and methods to manage memory on a spin transfer torque magnetoresistive random-access memory (STT-MRAM) are provided. A particular method of managing memory includes determining a temperature associated with the memory and determining a level of write queue utilization associated with the memory. A write operation may be performed based on the level of write queue utilization and the temperature.
Abstract:
According to one embodiment, a system is provided that includes at least one power gated component and two or more power switch transistors configured to provide one or more conductive paths between a common power supply rail, the at least one power gated component, and a ground. The two or more power switch transistors each include a source terminal, a drain terminal, and a gate terminal configured to control current flow between the source and drain terminals. The system also includes a rotating voltage control coupled to the gate terminals and configured to apply a sequence of control signals rotating between an on-state and an off-state to each of the gate terminals while the at least one power gated component is turned on. A switch activation ratio level is programmable to set a number of power switch transistors in the on-state relative to a total number of power switch transistors.
Abstract:
Embodiments include a method for managing power in a computer system including a main processor and an active memory device including powered units, the active memory device in communication with the main processor by a memory link, the powered units including a processing element. The method includes the main processor executing a program on a program thread, encountering a first section of code to be executed by the active memory device, changing, by a first command, a power state of a powered unit on the active memory device based on the main processor encountering the first section of code, the first command including a store command. The method also includes the processing element executing the first section of code at a second time, changing a power state of the main processor from a power use state to a power saving state based on the processing element executing the first section.
Abstract:
In a first device, a local classification and a local classification confidence score corresponding to an event input are computed. At the first device in response to a broadcast request, a remote classification and a remote classification confidence score corresponding to the event input are received, the remote classification and the remote classification confidence score being computed at a second device. At the first device, a consensus classification including the most frequent classification from a set of all received remote classifications and the local classification is formed, provided the number of classifications including the most frequent classification exceeds a threshold. In response to a consensus classification confidence score corresponding to the consensus classification exceeding a confidence threshold, a local classification model is updated. Based on the local classification and the consensus classification, the event input is assigned to a classification.
Abstract:
A system includes a determination component that determines output for successively larger neural networks of a set; and a consensus component that determines consensus between a first neural network and a second neural network of the set. A linear chain of increasingly complex neural networks trained on progressively larger inputs is utilized (e.g., increasingly complex neural networks is generally representative of increased accuracy). Outputs of progressively networks are computed until a consensus point is reached—where two or more successive large networks yield a same inference output. At such point of consensus the larger neural network of the set reaching consensus can be deemed appropriately sized (or of sufficient complexity) for a classification task at hand.
Abstract:
Various embodiments are provided for load balancing of machine learning operations in a computing environment by a processor. One or more machine learning operations performing inference or training operations may by dynamically balanced between one or more edge computing devices in a wireless communication network and a cloud computing system for increasing performance of a selected metric.
Abstract:
An N modular redundancy method, system, and computer program product include a computer-implemented N modular redundancy method for neural networks, the method including selectively replicating the neural network by employing one of checker neural networks and selective N modular redundancy (N-MR) applied only to critical computations.
Abstract:
Embodiments relate to an input-encoding technique in conjunction with federation. Participating entities are arranged in a collaborative relationship. Each participating entity trains a machine learning model with an encoder on a training data set. The performance of each of the models is measured and at least one of the models is selectively identified based on the measured performance. An encoder of the selectively identified machine learning model is shared with each of the participating entities. The shared encoder is configured to be applied by the participating entities to train the first and second machine learning models, which are configured to be merged and shared in the federated learning environment.