Concept

Accélération matérielle

Publications associées (416)

Accelerator-driven Data Arrangement to Minimize Transformers Run-time on Multi-core Architectures

David Atienza Alonso, Giovanni Ansaloni, Alireza Amirshahi

The increasing complexity of transformer models in artificial intelligence expands their computational costs, memory usage, and energy consumption. Hardware acceleration tackles the ensuing challenges by designing processors and accelerators tailored for t ...

2024

Verification and testing of hardware heavily relies on cycle-accurate simulation of RTL.As single-processor performance is growing only slowly, conventional, single-threaded RTL simulation is becoming impractical for increasingly complex chip designs and s ...

EPFL2024

X-Attack 2.0: The Risk of Power Wasters and Satisfiability Don’t-Care Hardware Trojans to Shared Cloud FPGAs

Mirjana Stojilovic, Dina Gamaleldin Ahmed Shawky Mahmoud, Beatrice Shokry Samir Shokry, Wei Hu

Cloud computing environments increasingly provision field-programmable gate arrays (FPGAs) for their programmability and hardware-level parallelism. While FPGAs are typically used by one tenant at a time, multitenant schemes supporting spatial sharing of c ...

2024

Bridging Monte Carlo worlds: a new framework for accelerator simulations applied on shielding and machine protection studies

André Donadon Servelle

This thesis delves into the critical study of particle transport in matter, particularly emphasisingits implications for machine protection at CERN's accelerator complex and facilities. To conductstudies of this nature, FLUKA and Geant4 stand out within a ...

EPFL2024

Tradeoffs in Low-Power Accelerators Design for Large-Scale Interferometers

David Atienza Alonso, Miguel Peon Quiros, Denisa-Andreea Constantinescu, Benoît Walter Denkinger

Large-scale scientific infrastructures like SKAO—the world’s largest radio observatory for the coming decades—are generating massive-scale data streams of multi-Tb/s to be processed using complex interferometry algorithms. Concretely, SKAO is expected to g ...

2023

Post-Moore's Law Fusion: High-Bandwidth Memory, Accelerators, and Native Half-Precision Processing for CPU-Local Analytics

Anastasia Ailamaki, Viktor Sanca

Modern data management systems aim to provide both cutting-edge functionality and hardware efficiency. With the advent of AI-driven data processing and the post-Moore Law era, traditional memory-bound scale-up data management operations face scalability ch ...

2023

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Embedded systems confront two opposite goals: low-power operation and high performance. The current trend to reach these goals is toward heterogeneous platforms, including multi-core architectures with heterogeneous cores and hardware accelerators. The lat ...

2023

Building Chips Faster: Hardware-Compiler Co-Design for Accelerated RTL Simulation

Sahand Kashani

The demise of Moore's Law and Dennard scaling has resulted in diminishing performance gains for general-purpose processors, and so has prompted a surge in academic and commercial interest for hardware accelerators.Specialized hardware has already redefined ...

EPFL2023

HetCache: Synergising NVMe Storage and GPU acceleration for Memory-Efficient Analytics

Anastasia Ailamaki, Periklis Chrysogelos, Hamish Mcniece Hill Nicholson, Syed Mohammad Aunn Raza

Accessing input data is a critical operation in data analytics: i) slow data access significantly degrades performance, and ii) storing everything in the fastest medium, i.e., memory, incurs high operational and hardware costs. Further, while GPUs offer in ...

2023

Bit-Line Computing for CNN Accelerators Co-Design in Edge AI Inference

David Atienza Alonso, Giovanni Ansaloni, Alexandre Sébastien Julien Levisse, Marco Antonio Rios, Flavio Ponzina

By supporting the access of multiple memory words at the same time, Bit-line Computing (BC) architectures allow the parallel execution of bit-wise operations in-memory. At the array periphery, arithmetic operations are then derived with little additional o ...

2023

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