Investigation of LSTM Based Prediction for Dynamic Energy Management in Chip Multiprocessors

In this paper, we investigate the effectiveness of using long short-term memory (LSTM) instead of Kalman filtering to do prediction for the purpose of constructing dynamic energy management (DEM) algorithms in chip multi-processors (CMPs). Either of the two prediction methods is employed to estimate the workload in the next control period for each of the processor cores. These estimates are then used to select voltage-frequency (VF) pairs for each core of the CMP during the next control period as part of a dynamic voltage and frequency scaling (DVFS) technique. The objective of the DVFS technique is to reduce energy consumption under performance constraints that are set by the user. We conduct our investigation using a custom Sniper system simulation framework. Simulation results for 16 and 64 core network-on-chip based CMP architectures and using several benchmarks demonstrate that the LSTM is slightly better than Kalman filtering

Investigation of LSTM Based Prediction for Dynamic Energy Management in Chip Multiprocessors

In this paper, we investigate the effectiveness of using long short-term memory (LSTM) instead of Kalman filtering to do prediction for the purpose of constructing dynamic energy management (DEM) algorithms in chip multi-processors (CMPs). Either of the two prediction methods is employed to estimate the workload in the next control period for each of the processor cores. These estimates are then used to select voltage-frequency (VF) pairs for each core of the CMP during the next control period as part of a dynamic voltage and frequency scaling (DVFS) technique. The objective of the DVFS technique is to reduce energy consumption under performance constraints that are set by the user. We conduct our investigation using a custom Sniper system simulation framework. Simulation results for 16 and 64 core network-on-chip based CMP architectures and using several benchmarks demonstrate that the LSTM is slightly better than Kalman filtering

Automatic Environmental Sound Recognition: Performance versus Computational Cost

In the context of the Internet of Things (IoT), sound sensing applications are required to run on embedded platforms where notions of product pricing and form factor impose hard constraints on the available computing power. Whereas Automatic Environmental Sound Recognition (AESR) algorithms are most often developed with limited consideration for computational cost, this article seeks which AESR algorithm can make the most of a limited amount of computing power by comparing the sound classification performance em as a function of its computational cost. Results suggest that Deep Neural Networks yield the best ratio of sound classification accuracy across a range of computational costs, while Gaussian Mixture Models offer a reasonable accuracy at a consistently small cost, and Support Vector Machines stand between both in terms of compromise between accuracy and computational cost

Deep Learning for Multiscale Damage Analysis via Physics-Informed Recurrent Neural Network

Direct numerical simulation of hierarchical materials via homogenization-based concurrent multiscale models poses critical challenges for 3D large scale engineering applications, as the computation of highly nonlinear and path-dependent material constitutive responses at the lower scale causes prohibitively high computational costs. In this work, we propose a physics-informed data-driven deep learning model as an efficient surrogate to emulate the effective responses of heterogeneous microstructures under irreversible elasto-plastic hardening and softening deformation. Our contribution contains several major innovations. First, we propose a novel training scheme to generate arbitrary loading sequences in the sampling space confined by deformation constraints where the simulation cost of homogenizing microstructural responses per sequence is dramatically reduced via mechanistic reduced-order models. Second, we develop a new sequential learner that incorporates thermodynamics consistent physics constraints by customizing training loss function and data flow architecture. We additionally demonstrate the integration of trained surrogate within the framework of classic multiscale finite element solver. Our numerical experiments indicate that our model shows a significant accuracy improvement over pure data-driven emulator and a dramatic efficiency boost than reduced models. We believe our data-driven model provides a computationally efficient and mechanics consistent alternative for classic constitutive laws beneficial for potential high-throughput simulations that needs material homogenization of irreversible behaviors

VGM-RNN: Recurrent Neural Networks for Video Game Music Generation

The recent explosion of interest in deep neural networks has affected and in some cases reinvigorated work in fields as diverse as natural language processing, image recognition, speech recognition and many more. For sequence learning tasks, recurrent neural networks and in particular LSTM-based networks have shown promising results. Recently there has been interest – for example in the research by Google’s Magenta team – in applying so-called “language modeling” recurrent neural networks to musical tasks, including for the automatic generation of original music. In this work we demonstrate our own LSTM-based music language modeling recurrent network. We show that it is able to learn musical features from a MIDI dataset and generate output that is musically interesting while demonstrating features of melody, harmony and rhythm. We source our dataset from VGMusic.com, a collection of user-submitted MIDI transcriptions of video game songs, and attempt to generate output which emulates this kind of music