arXiv subjects

Graham Riley

Publications and source records attributed to Graham Riley.

6 recordsLinked to original sources

arXiv2020

Energy Predictive Models for Convolutional Neural Networks on Mobile Platforms

Energy use is a key concern when deploying deep learning models on mobile and embedded platforms. Current studies develop energy predictive models based on application-level features to provide researchers a way to estimate the energy consumption of their deep learning models. This information is useful for building resource-aware models that can make efficient use of the hard-ware resources. However, previous works on predictive modelling provide little insight into the trade-offs involved in the choice of features on the final predictive model accuracy and model complexity. To address this issue, we provide a comprehensive analysis of building regression-based predictive models for deep learning on mobile devices, based on empirical measurements gathered from the SyNERGY framework.Our predictive modelling strategy is based on two types of predictive models used in the literature:individual layers and layer-type. Our analysis of predictive models show that simple layer-type features achieve a model complexity of 4 to 32 times less for convolutional layer predictions for a similar accuracy compared to predictive models using more complex features adopted by previous approaches. To obtain an overall energy estimate of the inference phase, we build layer-type predictive models for the fully-connected and pooling layers using 12 representative Convolutional NeuralNetworks (ConvNets) on the Jetson TX1 and the Snapdragon 820using software backends such as OpenBLAS, Eigen and CuDNN. We obtain an accuracy between 76% to 85% and a model complexity of 1 for the overall energy prediction of the test ConvNets across different hardware-software combinations.

cs.PF↗

arXiv2018

Navigating the Landscape for Real-time Localisation and Mapping for Robotics and Virtual and Augmented Reality

Visual understanding of 3D environments in real-time, at low power, is a huge computational challenge. Often referred to as SLAM (Simultaneous Localisation and Mapping), it is central to applications spanning domestic and industrial robotics, autonomous vehicles, virtual and augmented reality. This paper describes the results of a major research effort to assemble the algorithms, architectures, tools, and systems software needed to enable delivery of SLAM, by supporting applications specialists in selecting and configuring the appropriate algorithm and the appropriate hardware, and compilation pathway, to meet their performance, accuracy, and energy consumption goals. The major contributions we present are (1) tools and methodology for systematic quantitative evaluation of SLAM algorithms, (2) automated, machine-learning-guided exploration of the algorithmic and implementation design space with respect to multiple objectives, (3) end-to-end simulation tools to enable optimisation of heterogeneous, accelerated architectures for the specific algorithmic requirements of the various SLAM algorithmic approaches, and (4) tools for delivering, where appropriate, accelerated, adaptive SLAM solutions in a managed, JIT-compiled, adaptive runtime context.

cs.CV↗

arXiv2018

Fine-Grained Energy and Performance Profiling framework for Deep Convolutional Neural Networks

There is a huge demand for on-device execution of deep learning algorithms on mobile and embedded platforms. These devices present constraints on the application due to limited resources and power. Hence, developing energy-efficient solutions to address this issue will require innovation in algorithmic design, software and hardware. Such innovation requires benchmarking and characterization of Deep Neural Networks based on performance and energy-consumption alongside accuracy. However, current benchmarks studies in existing deep learning frameworks (for example, Caffe, Tensorflow, Torch and others) are based on performance of these applications on high-end CPUs and GPUs. In this work, we introduce a benchmarking framework called "SyNERGY" to measure the energy and time of 11 representative Deep Convolutional Neural Networks on embedded platforms such as NVidia Jetson TX1. We integrate ARM's Streamline Performance Analyser with standard deep learning frameworks such as Caffe and CuDNNv5, to study the execution behaviour of current deep learning models at a fine-grained level (or specific layers) on image processing tasks. In addition, we build an initial multi-variable linear regression model to predict energy consumption of unseen neural network models based on the number of SIMD instructions executed and main memory accesses of the CPU cores of the TX1 with an average relative test error rate of 8.04 +/- 5.96 %. Surprisingly, we find that it is possible to refine the model to predict the number of SIMD instructions and main memory accesses solely from the application's Multiply-Accumulate (MAC) counts, hence, eliminating the need for actual measurements. Our predicted results demonstrate 7.08 +/- 6.0 % average relative error over actual energy measurements of all 11 networks tested, except MobileNet. By including MobileNet the average relative test error increases to 17.33 +/- 12.2 %.

cs.PF↗

arXiv2017

Vectorization of Hybrid Breadth First Search on the Intel Xeon Phi

The Breadth-First Search (BFS) algorithm is an important building block for graph analysis of large datasets. The BFS parallelisation has been shown to be challenging because of its inherent characteristics, including irregular memory access patterns, data dependencies and workload imbalance, that limit its scalability. We investigate the optimisation and vectorisation of the hybrid BFS (a combination of top-down and bottom-up approaches for BFS) on the Xeon Phi, which has advanced vector processing capabilities. The results show that our new implementation improves by 33\%, for a one million vertices graph, compared to the state-of-the-art.

cs.DC↗

arXiv2016

Breadth First Search Vectorization on the Intel Xeon Phi

Breadth First Search (BFS) is a building block for graph algorithms and has recently been used for large scale analysis of information in a variety of applications including social networks, graph databases and web searching. Due to its importance, a number of different parallel programming models and architectures have been exploited to optimize the BFS. However, due to the irregular memory access patterns and the unstructured nature of the large graphs, its efficient parallelization is a challenge. The Xeon Phi is a massively parallel architecture available as an off-the-shelf accelerator, which includes a powerful 512 bit vector unit with optimized scatter and gather functions. Given its potential benefits, work related to graph traversing on this architecture is an active area of research. We present a set of experiments in which we explore architectural features of the Xeon Phi and how best to exploit them in a top-down BFS algorithm but the techniques can be applied to the current state-of-the-art hybrid, top-down plus bottom-up, algorithms. We focus on the exploitation of the vector unit by developing an improved highly vectorized OpenMP parallel algorithm, using vector intrinsics, and understanding the use of data alignment and prefetching. In addition, we investigate the impact of hyperthreading and thread affinity on performance, a topic that appears under researched in the literature. As a result, we achieve what we believe is the fastest published top-down BFS algorithm on the version of Xeon Phi used in our experiments. The vectorized BFS top-down source code presented in this paper can be available on request as free-to-use software.

cs.DC↗

arXiv2014

Introducing SLAMBench, a performance and accuracy benchmarking methodology for SLAM

Real-time dense computer vision and SLAM offer great potential for a new level of scene modelling, tracking and real environmental interaction for many types of robot, but their high computational requirements mean that use on mass market embedded platforms is challenging. Meanwhile, trends in low-cost, low-power processing are towards massive parallelism and heterogeneity, making it difficult for robotics and vision researchers to implement their algorithms in a performance-portable way. In this paper we introduce SLAMBench, a publicly-available software framework which represents a starting point for quantitative, comparable and validatable experimental research to investigate trade-offs in performance, accuracy and energy consumption of a dense RGB-D SLAM system. SLAMBench provides a KinectFusion implementation in C++, OpenMP, OpenCL and CUDA, and harnesses the ICL-NUIM dataset of synthetic RGB-D sequences with trajectory and scene ground truth for reliable accuracy comparison of different implementation and algorithms. We present an analysis and breakdown of the constituent algorithmic elements of KinectFusion, and experimentally investigate their execution time on a variety of multicore and GPUaccelerated platforms. For a popular embedded platform, we also present an analysis of energy efficiency for different configuration alternatives.

cs.RO↗