Apps with hardware: enabling run-time architectural customization in smart phones Coughlin et al., USENIX ATC'16 This week we've had a couple of hardware-related papers, and one touching on mobile apps (in the context of DNNs). Today's choice brings those themes together with some really creative thinking - programmable hardware for smartphones! With thanks to Afshaan … Continue reading Apps with hardware: enabling run-time architectural customization in smart phones
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Neurosurgeon: collaborative intelligence between the cloud and the mobile edge
Neurosurgeon: collaborative intelligence between the cloud and mobile edge Kang et al., ASPLOS'17 For a whole class of new intelligent personal assistant applications that process images, videos, speech, and text using deep neural networks, the common wisdom is that you really need to run the processing in the cloud to take advantage of powerful clusters … Continue reading Neurosurgeon: collaborative intelligence between the cloud and the mobile edge
Bolt: I know what you did last summer… in the cloud
Bolt: I know what you did last summer... in the cloud Delimitrou & Kozyrakis, ASPLOS'17 You get your run-of-the-mill noisy neighbours - the ones who occasionally have friends round and play music a little too loud until a little too late. And then in the UK at least you get what we used to call … Continue reading Bolt: I know what you did last summer… in the cloud
Determining application-specific peak power and energy requirements for ultra-low power processors
Determining application-specific peak power and energy requirements for ultra-low power processors Cherupalli et al., ASPLOS'17 We're straying a little bit out of The Morning Paper comfort zone again this morning to look at one of the key hardware issues affecting the design of IoT devices: how much energy they use, and the related question of … Continue reading Determining application-specific peak power and energy requirements for ultra-low power processors
Typed Architectures: architectural support for lightweight scripting
Typed Architectures: architectural support for lightweight scripting Kim et al., ASPLOS'17 JavaScript, Python, Ruby, Lua, and related dynamically typed scripting languages are increasingly popular for developing IoT applications. For example, the Raspberry Pi is associated with Python; Arduino and Intel's Galileo and Edison are associated with JavaScript. In these constrained hardware environments though, using JITs … Continue reading Typed Architectures: architectural support for lightweight scripting
Who controls the Internet? Analyzing global threats using property traversal graphs
Who controls the Internet? Analyzing global threats using property traversal graphs Simeonovski et al., WWW'17 Who controls the Internet? How much influence do they have? And what would happen if one of those parties launched an attack or was compromised and used to launch an attack? Previous works have looked at the individual core services, … Continue reading Who controls the Internet? Analyzing global threats using property traversal graphs
BOAT: Building auto-tuners with structured Bayesian optimization
BOAT: Building auto-tuners with structured Bayesian optimization Dalibard et al., WWW'17 Due to their complexity, modern systems expose many configuration parameters which users must tune to maximize performance... From the number of machines used in a distributed application, to low-level parameters such as compiler flags, managing configurations has become one of the main challenges faced … Continue reading BOAT: Building auto-tuners with structured Bayesian optimization
DeepSense: a unified deep learning framework for time-series mobile sensing data processing
DeepSense: a unified deep learning framework for time-series mobile sensing data processing Yao et al., WWW'17 DeepSense is a deep learning framework that runs on mobile devices, and can be used for regression and classification tasks based on data coming from mobile sensors (e.g., motion sensors). An example of a classification task is heterogeneous human … Continue reading DeepSense: a unified deep learning framework for time-series mobile sensing data processing
Usage patterns and the economics of the public cloud
Usage patterns and the economics of the public cloud Kilcioglu et al., WWW'17 Illustrating the huge diversity of topics covered at WWW, following yesterday's look at recovering mobile user trajectories from aggregate data, today's choice studies usage variation and pricing models in the public cloud. The basis for the study is data from 'a major … Continue reading Usage patterns and the economics of the public cloud
Trajectory recovery from Ash: User privacy is NOT preserved in aggregated mobility data
Trajectory recovery from Ash: User privacy is NOT preserved in aggregated mobility data Xu et al., WWW'17 Borrowing a little from Simon Wardley's marvellous Enterprise IT Adoption Cycle, here's roughly how my understanding progressed as I read through this paper: Huh? What? How? Nooooo, Oh No, Oh s*@\#! Xu et al. show us that even … Continue reading Trajectory recovery from Ash: User privacy is NOT preserved in aggregated mobility data
the morning paper 