We propose an energy-aware self-optimizing memory controller in which DRAM energy management is integral to the command scheduler. Experiments conducted on a 8-core CMP model show that, for the parallel applications considered, our scheduler reduces memory’s energy-delay squared Et by 18% while delivering a 5% speedup with respect to a state-of-the-art power-aware solution.

This paper optimizes the performance of the growing cell structures (GCS) model in learning topology and vector quantization. Each node in GCS is attached with a resource counter. During the competitive learning process, the counter of the best-matching node is increased by a defined resource measure after each input presentation, and then all resource counters are decayed by a factor alpha. We...

We present a variational Expectation-Maximization algorithm to learn probabilistic mixture models. The algorithm is similar to Kohonen’s Self-Organizing Map algorithm and not limited to Gaussian mixtures. We maximize the variational free-energy that sums data loglikelihood and Kullback-Leibler divergence between a normalized neighborhood function and the posterior distribution on the components...

This paper describes a parallel implementation of a matrix/vector library for C++ for a large distributed-memory multicomputer. The library is “self-optimising” by exploiting lazy evaluation: execution of matrix operations is delayed as much as possible. This exposes the context in which each intermediate result is used. The run-time system extracts a functional representation of the values bei...

In this paper we propose an intelligent agent approach to control the electric power grid at a smaller granularity in order to give it self-healing capabilities. We develop a method using the influence model to transform transmission substations into information processing, analyzing and decision making (intelligent behavior) units. We also develop a wireless communication method to deliver rea...

Religiosity is related to a variety of positive outcomes and the nature of this relationship has long been a topic of inquiry. Recently, it was proposed that an important piece of this puzzle may be the propensity for religious beliefs to promote self-control, a trait that is linked to a range of benefits. How religion translates into self-control, however, remains unclear. We examined the exte...

We developed a self-optimizing decision system that dynamically minimizes the overall energy consumption of an industrial process. Our model is based on deep reinforcement learning (DRL) framework, adopting three methods, namely: Q-network (DQN), proximal policy optimization (PPO), and advantage actor–critic (A2C) algorithms, combined with self-predicting random forest model. This smart physics...