Towards General-Purpose Decentralized Computing with Permissionless Extensibility

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Distributed systems designers typically strive to improve performance and preserve availability despite failures or attacks; but, when strong consistency is also needed, they encounter fundamental limitations. The bottleneck is in replica coordination, whi ...

Quantum many-body control is a central milestone en route to harnessing quantum technologies. However, the exponential growth of the Hilbert space dimension with the number of qubits makes it challenging to classically simulate quantum many-body systems an ...

Distributed ledgers provide high availability and integrity, making them a key enabler for practical and secure computation of distributed workloads among mutually distrustful parties. Many practical applications also require strong confidentiality, howeve ...

Side-channel attacks exploit a physical observable originating from a cryptographic device in order to extract its secrets. Many practically relevant advances in the field of side-channel analysis relate to security evaluations of cryptographic functions a ...

Quantum two-level systems, i.e., qubits, form the basis for most quantum machine learning approaches that have been proposed throughout the years. However, higher dimensional quantum systems constitute a promising alternative and are increasingly explored ...

DNN inference accelerators executing online services exhibit low average loads because of service demand variability, leading to poor resource utilization. Unfortunately, reclaiming idle inference cycles is difficult as other workloads can not execute on a ...

The study of non-contact manipulation in water, and the ability to robotically control floating objects has gained recent attention due to wide-ranging potential applications, including the analysis of plastic pollution in the oceans and the optimization o ...

Decentralized training of deep learning models enables on-device learning over networks, as well as efficient scaling to large compute clusters. Experiments in earlier works reveal that, even in a data-center setup, decentralized training often suffers fro ...

Profound changes driven by decarbonization, decentralization, and digitalization are disrupting the energy industry, bringing new challenges to its key stakeholders. In the attempt to address the climate change issue, increasing penetration of renewables a ...

Centralized and cloud computing-based network architectures are the promising tracks of future communication systems where a large scale compute power can be virtualized for various algorithms. These architectures rely on high-performance communication lin ...

As the volume of produced data is exponentially increasing, companies tend to rely on distributed systems to meet the surging demand for storage capacity. With the business workflows becoming more and more complex, such systems often consist of or are acce ...

Deep neural network inference accelerators are deployed at scale to accommodate online services, but face low average load because of service demand variability, leading to poor resource utilization. Unfortunately, reclaiming inference idle cycles is diffi ...

Deep neural networks (DNNs) have revolutionized the field of artificial intelligence and have achieved unprecedented success in cognitive tasks such as image and speech recognition. Training of large DNNs, however, is computationally intensive and this has ...

Strong gravitational lensing is a powerful probe of cosmology and the dark matter distribution. Efficient lensing software is already a necessity to fully use its potential and the performance demands will only increase with the upcoming generation of tele ...

Advances in deep neural networks (DNN) and distributed ledger technology (DLT) have shown major influence on media security, authenticity and privacy. Current deepfake techniques can produce near realistic media content which can be used in both good and b ...

Demand Response (DR) is progressively moving from a centralized, unidirectional structure to a set of advanced decentralized mechanisms that better balance distributed supply and demand. This paper presents a decentralized cooperative DR framework to manag ...

Supply Chain Finance (SCF) refers to the financial service in which banks rely on core enterprises to manage the capital flow and logistics of upstream and downstream enterprises. SCF adopts a self-testing and closed-loop credit model to control funds and ...

Deep neural networks (DNNs) have surpassed human-level accuracy in a variety of cognitive tasks but at the cost of significant memory/time requirements in DNN training. This limits their deployment in energy and memory limited applications that require rea ...

Towards General-Purpose Decentralized Computing with Permissionless Extensibility

Chattez avec Graph Search

Building Strongly-Consistent Systems Resilient to Failures, Partitions, and Slowdowns

Self-correcting quantum many-body control using reinforcement learning with tensor networks

CALYPSO: Private Data Management for Decentralized Ledgers

The Side-channel Metrics Cheat Sheet

Data re-uploading with a single qudit

Equinox: Training (for Free) on a Custom Inference Accelerator

Non-contact robotic manipulation of floating objects: exploiting emergent limit cycles

Consensus Control for Decentralized Deep Learning

A Blockchain-Supported Framework for Charging Management of Electric Vehicles

Modified Joint Channel-and-Data Estimation for One-Bit Massive MIMO

Clouseau: Blockchain-based Data Integrity for HDFS Clusters

ColTraIn: Co-located DNN training and inference

Mixed-Precision Deep Learning Based on Computational Memory

High Performance Computing for gravitational lens modeling: Single vs double precision on GPUs and CPUs

A Media Security Framework Inspired by Emerging Challenges in Fake Media and NFT

Cooperative energy management of a community of smart-buildings: A Blockchain approach

A blockchain and IoT-based lightweight framework for enabling information transparency in supply chain finance

ESSOP: Efficient and Scalable Stochastic Outer Product Architecture for Deep Learning