On the future of research VMs: a hardware/software perspective

Zakkak, Foivos S.; Nisbet, Andy; Mawer, John; Hartley, Tim; Foutris, Nikos; Papadakis, Orion; Andronikakis, Andreas; Apreotesei, Iain; Kotselidis, Christos

doi:10.1145/3191697.3191729

Cited by 1 publication

(1 citation statement)

References 19 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…Consequently, we have chosen to use and extend the Oracle Labs research project software for MaxineVM [75] that provided a state-of-the-art research VM for x86-64. We have developed a robust port of MaxineVM to ARMv7 [71], [76] (an AArch64 port is also in progress) ISA processors that can run important Java and SLAM benchmarks, including a Java version of KinectFusion. MaxineVM has been designed for maximum flexibility, this sacrifices some performance, but it is trivially possible to replace the public implementation of an interface or scheme, such as for monitor or garbage collection with simple command line switches to the command that generates a MaxineVM executable image.…”

Section: B Dynamic Schedulingmentioning

confidence: 99%

Navigating the Landscape for Real-Time Localization and Mapping for Robotics and Virtual and Augmented Reality

et al. 2018

Self Cite

View full text Add to dashboard Cite

Visual understanding of 3D environments in realtime, 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. Performance EvaluationRuntime Architecture Compiler and Algorithm Design Space Exploration -Machine Learning Fig. 1: The objective of the paper is to create a pipeline that aligns computer vision requirements with hardware capabilities. The paper's focus is on three layers: algorithms, compiler and runtime, and architecture. The goal is to develop a system that allows us to achieve power and energy efficiency, speed and runtime improvement, and accuracy/robustness at each layer and also holistically through design space exploration and machine learning techniques.

show abstract

Section: B Dynamic Schedulingmentioning

confidence: 99%