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Pervasive Computing with Frugal Objects
Related publications (33)
Exploring High-Performance and Energy-Efficient Architectures for Edge AI-Enabled Applications
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The desire and ability to place AI-enabled applications on the edge has grown significantly in recent years. However, the compute-, area-, and power-constrained nature of edge devices are stressed by the needs of the AI-enabled applications, due to a gener ...
EPFL2024Special Session: Challenges and Opportunities for Sustainable Multi-Scale Computing Systems
David Atienza Alonso, Miguel Peon Quiros
Multi-Scale computing systems aim at bringing the computing as close as possible to the data sources, to optimize both computation and networking. These systems are composed of at least three computing layers: the terminal layer, the edge layer, and the cl ...
ACM2023Massively parallel nodal discontinous Galerkin finite element method simulator for room acoustics
We present a massively parallel and scalable nodal discontinuous Galerkin finite element method (DGFEM) solver for the time-domain linearized acoustic wave equations. The solver is implemented using the libParanumal finite element framework with extensions ...
London20232D Nanosystems: Applications of 2D Semiconductors for In-Memory Computing
Machine learning and data processing algorithms have been thriving in finding ways of processing and classifying information by exploiting the hidden trends of large datasets. Although these emerging computational methods have become successful in today's ...
EPFL2023Imprecise Store Exceptions
Babak Falsafi, Mathias Josef Payer, Yuanlong Li, Siddharth Gupta, Yunho Oh, Qingxuan Kang, Abhishek Bhattacharjee
Precise exceptions are a cornerstone of modern computing as they provide the abstraction of sequential instruction execution to programmers while accommodating microarchitectural optimizations. However, increasing compute capabilities in deep memory hierar ...
ACM2023System Support for Robust Distributed Learning
Arsany Hany Abdelmessih Guirguis
Machine learning (ML) applications are ubiquitous. They run in different environments such as datacenters, the cloud, and even on edge devices. Despite where they run, distributing ML training seems the only way to attain scalable, high-quality learning. B ...
EPFL2022Pipeline Synthesis and Optimization from Branched Feedback Dataflow Programs
Marco Mattavelli, Simone Casale Brunet, Endri Bezati, Anatoly Prihozhy
Large dataflow designs are a result of behavioral specification of modern complex digital systems and/or a result of unfolding and transforming looped and branched programs. Since deep-submicron silicon technology provides large amounts of available resour ...
SPRINGER2020Task scheduling for highly concurrent analytical and transaction workloads
Anastasia Ailamaki, Iraklis Psaroudakis
Systems and method for a task scheduler with dynamic adjustment of concurrency levels and task granularity are disclosed for improved execution of highly concurrent analytical and transactional systems. The task scheduler can avoid both over commitment and ...
2020Integrating Heuristic and Machine-Learning Methods for Efficient Virtual Machine Allocation in Data Centers
David Atienza Alonso, Marina Zapater Sancho, Ali Pahlevan, Xiaoyu Qu
Modern cloud data centers (DCs) need to tackle efficiently the increasing demand for computing resources and address the energy efficiency challenge. Therefore, it is essential to develop resource provisioning policies that are aware of virtual machine (VM ...
2018Logic Synthesis for Established and Emerging Computing
Giovanni De Micheli, Mathias Soeken, Luca Gaetano Amarù, Eleonora Testa
Logic synthesis is an enabling technology to realize integrated computing systems, and it entails solving computationally intractable problems through a plurality of heuristic techniques. A recent push toward further formalization of synthesis problems has ...
2018