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Technical Perspective: Tracking Pandemic-Driven Internet Traffic
From Communications of the ACM

Technical Perspective: Tracking Pandemic-Driven Internet Traffic

"A Year in Lockdown," by Anja Feldmann, et al., offers a detailed look at how Internet traffic changed during the COVID-19 pandemic.

A Year in Lockdown
From Communications of the ACM

A Year in Lockdown: How the Waves of COVID-19 Impact Internet Traffic

We review the impact of the first year of the COVID-19 pandemic on Internet traffic in order to analyze its performance.

Technical Perspective: An Elegant Model for Deriving Equations
From Communications of the ACM

Technical Perspective: An Elegant Model for Deriving Equations

"Deriving Equations from Sensor Data Using Dimensional Function Synthesis," by Vasileios Tsoutsouras, et al., addresses the key problem of discovering relationships...

Deriving Equations from Sensor Data Using Dimensional Function Synthesis
From Communications of the ACM

Deriving Equations from Sensor Data Using Dimensional Function Synthesis

We present a new method, which we call dimensional function synthesis, for deriving functions that model the relationship between multiple signals in a physical...

From Communications of the ACM

Technical Perspective: Race Logic Presents a Novel Form of Encoding

"In-Sensor Classification With Boosted Race Trees," by Georgios Tzimpragos, et al., proposes a surprising, novel, and creative approach to post-Moore's Law computing...

In-Sensor Classification With Boosted Race Trees
From Communications of the ACM

In-Sensor Classification With Boosted Race Trees

We demonstrate the potential of a novel form of encoding, race logic, in which information is represented as the delay in the arrival of a signal.

From Communications of the ACM

Technical Perspective: A Chiplet Prototype System for Deep Learning Inference

"Simba," by Yakun Sophia Shao, et al., presents a scalable deep learning accelerator architecture that tackles issues ranging from chip integration technology to...

Simba
From Communications of the ACM

Simba: Scaling Deep-Learning Inference with Chiplet-Based Architecture

This work investigates and quantifies the costs and benefits of using multi-chip-modules with fine-grained chiplets for deep learning inference, an application...

From Communications of the ACM

Technical Perspective: Solving the Signal Reconstruction Problem at Scale

"Scalable Signal Reconstruction for a Broad Range of Applications," by Abolfazl Asudeh, et al. shows that algorithmic insights about SRP, combined with database...

Scalable Signal Reconstruction for a Broad Range of Applications
From Communications of the ACM

Scalable Signal Reconstruction for a Broad Range of Applications

Most of the common approaches for solving signal reconstruction problem do not scale to large problem sizes. We propose a novel and scalable algorithm for solving...

From Communications of the ACM

Technical Perspective: SkyCore's Architecture Takes It to the 'Edge'

"SkyCore," by Mehrdad Moradi, et al., addresses an exciting use case for Unmanned Aerial Vehicles in which UAVs can act as mobile base stations for the cellular...

SkyCore
From Communications of the ACM

SkyCore: Moving Core to the Edge for Untethered and Reliable UAV-Based LTE Networks

We argue for and propose an alternate, radical edge evolved packet core design, called SkyCore, that pushes the EPC functionality to the extreme edge of the core...

From Communications of the ACM

Technical Perspective: Deciphering Errors to Reduce the Cost of Quantum Computation

In "Constant Overhead Quantum Fault Tolerance with Quantum Expander Codes," by Omar Fawzi, et al., the authors produce an algorithm that can rapidly deduce the...

Constant Overhead Quantum Fault Tolerance with Quantum Expander Codes
From Communications of the ACM

Constant Overhead Quantum Fault Tolerance with Quantum Expander Codes

In this paper, we study the asymptotic scaling of the space overhead needed for fault-tolerant quantum computation.

From Communications of the ACM

Technical Perspective: The Future of Large-Scale Embedded Sensing

The system described in "SATURN: An Introduction to the Internet of Materials" works passively, energized essentially by static electricity generated as layers...

SATURN
From Communications of the ACM

SATURN: An Introduction to the Internet of Materials

We propose an Internet of Materials, where the very materials of objects and surfaces are augmented or manufactured to have computational capabilities.

From Communications of the ACM

Technical Perspective: XNOR-Networks – Powerful but Tricky

How to produce a convolutional neural net that is small enough to run on a mobile device, and accurate enough to be worth using? The strategies in "Enabling AI...

Enabling AI at the Edge with XNOR-Networks
From Communications of the ACM

Enabling AI at the Edge with XNOR-Networks

We present a novel approach to running state-of-the-art AI algorithms in edge devices, and propose two efficient approximations to standard convolutional neural...

From Communications of the ACM

Technical Perspective: ASIC Clouds: Specializing the Datacenter

Can we build purpose-built, warehouse-scale datacenters customized for large-scale arrays of ASIC accelerators or, to use a term coined in the paper by Michael...

ASIC Clouds
From Communications of the ACM

ASIC Clouds: Specializing the Datacenter for Planet-Scale Applications

This paper distills lessons from Bitcoin ASIC Clouds and applies them to other large scale workloads, showing superior TCO (total cost of ownership) versus CPU...
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