Abstract: Reinforcement learning enables a framework of information technology assets that include software elements, computational hardware assets, and/or, bundled software and computational hardware systems and products. The performance of successive sessions of an inner loop reinforcement learning is directed and monitored by an outer loop reinforcement learning wherein the outer loop reinforcement learning is designed to reduce financial costs and computational asset requirements and/or optimize learning time in successive instantiations of inner loop reinforcement learning training sessions. The framework enables consideration of the license costs of domain specific simulators, the usage cost of hardware platforms, and the progress of a particular reinforcement learning training. The framework further enables reductions of these costs to orchestrate and train a neural network under budget constraints with respect to the available hardware and software licenses available at runtime.

Abstract: Embodiments of the present disclosure provide a 6-lead electrocardiogram (ECG) monitoring device that may acquire a 6-lead ECG, does not require the use of adhesives for electrodes, provides ECG data for a user on a near instantaneous basis, and provides an easy and non-invasive way for a user to take a 6-lead ECG on the fly. The ECG monitoring device may acquire leads I, II, and III. The ECG monitoring device may derive augmented limb leads to generate a 6-lead ECG and may subsequently generate a full 12-lead ECG. The ECG monitoring device may generate one or more diagnoses based on the 6-lead reading or the 12-lead set.

Abstract: Reinforcement learning enables a framework of information technology assets that include software elements, computational hardware assets, and/or, bundled software and computational hardware systems and products. The performance of successive sessions of an inner loop reinforcement learning is directed and monitored by an outer loop reinforcement learning wherein the outer loop reinforcement learning is designed to reduce financial costs and computational asset requirements and/or optimize learning time in successive instantiations of inner loop reinforcement learning training sessions. The framework enables consideration of the license costs of domain specific simulators, the usage cost of hardware platforms, and the progress of a particular reinforcement learning training. The framework further enables reductions of these costs to orchestrate and train a neural network under budget constraints with respect to the available hardware and software licenses available at runtime.

Abstract: A scalable neural network system may include a root processor and a plurality of neural network processors with a tree of synchronizing sub-systems connecting them together. Each synchronization sub-system may connect one parent to a plurality of children. Furthermore, each of the synchronizing sub-systems may simultaneously distribute weight updates from the root processor to the plurality of neural network processors, while statistically combining corresponding weight gradients from its children into single statistical weight gradients. A generalized network of sensor-controllers may have a similar structure.

Abstract: A multi-processor system for batched pattern recognition may utilize a plurality of different types of neural network processors and may perform batched sets of pattern recognition jobs on a two-dimensional array of inner product units (IPUs) by iteratively applying layers of image data to the IPUs in one dimension, while streaming neural weights from an external memory to the IPUs in the other dimension. The system may also include a load scheduler, which may schedule batched jobs from multiple job dispatchers, via initiators, to one or more batched neural network processors for executing the neural network computations.

Abstract: A multi-processor system for data processing may utilize a plurality of different types of neural network processors to perform, e.g., learning and pattern recognition. The system may also include a scheduler, which may select from the available units for executing the neural network computations, which units may include standard multi-processors, graphic processor units (GPUs), virtual machines, or neural network processing architectures with fixed or reconfigurable interconnects.