Improving Mobile SOC\u27s Performance as an Energy Efficient DSP Platform with Heterogeneous Computing

Mobile system-on-chip (SOC) technology is improving at a staggering rate spurred primarily by the adoption of smartphones and tablets. This rapid innovation has allowed the mobile SOC to be considered in everything from high performance computing to embedded applications. In this work, modern SOC\u27s heterogeneous computing capabilities are evaluated with a focus toward digital signal processing (DSP). Evaluation is conducted on modern consumer devices running Android operating system and leveraging the relatively new RenderScript Compute to utilize CPU resources alongside other compute resources such as graphics processing units (GPUs) and digital signal processors. In order to benchmark these concepts, several implementations of both the discrete Fourier transform (DFT) and the fast Fourier transform (FFT) are tested across devices. The results show both improvement in performance and energy efficiency on many devices compared to traditional Java implementations and indicate that the mobile SOC is a relevant platform for DSP applications

Efficient Object Detection in Mobile and Embedded Devices with Deep Neural Networks

[EN] Neural networks have become the standard for high accuracy computer vision. These algorithms can be built with arbitrarily large architectures to handle an ever growing complexity in the data they process. State of the art neural network architectures are primarily concerned with increasing the recognition accuracy when performing inference on an image, which creates an insatiable demand for energy and compute power. These models are primarily targeted to run on dense compute units such as GPUs. In recent years, demand to allow these models to execute in limited capacity environments such as smartphones, however even the most compact variations of these state of the art networks constantly push the boundaries of the power envelop under which they run. With the emergence of the Internet of Things, it is becoming a priority to enable mobile systems to perform image recognition at the edge, but with small energy requirements. This thesis focuses on the design and implementation of an object detection neural network that attempts to solve this problem, providing reasonable accuracy rates with extremely low compute power requirements. This is achieved by re-imagining the meta architecture of traditional object detection models and discovering a mechanism to classify and localize objects through a set of neural network based algorithms that are better aimed to mobile and embedded devices. The main contributions of this thesis are: (i) provide a better image processing algorithm that is more suitable at preparing data for consumption by taking advantage of the characteristics of the ISP available in these devices; (ii) provide a neural network architecture that maintains acceptable accuracy targets with minimal computational requirements by making efficient use of basic neural algorithms; and (iii) provide a programming framework for how these systems can be most efficiently implemented in a manner that is optimized for the underlying hardware units available in these devices by taking into account memory and computation restrictions

Edge Intelligence : Empowering Intelligence to the Edge of Network

Edge intelligence refers to a set of connected systems and devices for data collection, caching, processing, and analysis proximity to where data are captured based on artificial intelligence. Edge intelligence aims at enhancing data processing and protects the privacy and security of the data and users. Although recently emerged, spanning the period from 2011 to now, this field of research has shown explosive growth over the past five years. In this article, we present a thorough and comprehensive survey of the literature surrounding edge intelligence. We first identify four fundamental components of edge intelligence, i.e., edge caching, edge training, edge inference, and edge offloading based on theoretical and practical results pertaining to proposed and deployed systems. We then aim for a systematic classification of the state of the solutions by examining research results and observations for each of the four components and present a taxonomy that includes practical problems, adopted techniques, and application goals. For each category, we elaborate, compare, and analyze the literature from the perspectives of adopted techniques, objectives, performance, advantages and drawbacks, and so on. This article provides a comprehensive survey of edge intelligence and its application areas. In addition, we summarize the development of the emerging research fields and the current state of the art and discuss the important open issues and possible theoretical and technical directions.Peer reviewe

Edge Intelligence : Empowering Intelligence to the Edge of Network

Edge intelligence refers to a set of connected systems and devices for data collection, caching, processing, and analysis proximity to where data are captured based on artificial intelligence. Edge intelligence aims at enhancing data processing and protects the privacy and security of the data and users. Although recently emerged, spanning the period from 2011 to now, this field of research has shown explosive growth over the past five years. In this article, we present a thorough and comprehensive survey of the literature surrounding edge intelligence. We first identify four fundamental components of edge intelligence, i.e., edge caching, edge training, edge inference, and edge offloading based on theoretical and practical results pertaining to proposed and deployed systems. We then aim for a systematic classification of the state of the solutions by examining research results and observations for each of the four components and present a taxonomy that includes practical problems, adopted techniques, and application goals. For each category, we elaborate, compare, and analyze the literature from the perspectives of adopted techniques, objectives, performance, advantages and drawbacks, and so on. This article provides a comprehensive survey of edge intelligence and its application areas. In addition, we summarize the development of the emerging research fields and the current state of the art and discuss the important open issues and possible theoretical and technical directions.Peer reviewe

An automated OpenCL FPGA compilation framework targeting a configurable, VLIW chip multiprocessor

Modern system-on-chips augment their baseline CPU with coprocessors and accelerators to increase overall computational capacity and power efficiency, and thus have evolved into heterogeneous systems. Several languages have been developed to enable this paradigm shift, including CUDA and OpenCL. This thesis discusses a unified compilation environment to enable heterogeneous system design through the use of OpenCL and a customised VLIW chip multiprocessor (CMP) architecture, known as the LE1. An LLVM compilation framework was researched and a prototype developed to enable the execution of OpenCL applications on the LE1 CPU. The framework fully automates the compilation flow and supports work-item coalescing to better utilise the CPU cores and alleviate the effects of thread divergence. This thesis discusses in detail both the software stack and target hardware architecture and evaluates the scalability of the proposed framework on a highly precise cycle-accurate simulator. This is achieved through the execution of 12 benchmarks across 240 different machine configurations, as well as further results utilising an incomplete development branch of the compiler. It is shown that the problems generally scale well with the LE1 architecture, up to eight cores, when the memory system becomes a serious bottleneck. Results demonstrate superlinear performance on certain benchmarks (x9 for the bitonic sort benchmark with 8 dual-issue cores) with further improvements from compiler optimisations (x14 for bitonic with the same configuration