Publication

Flexible Hardware Acceleration for Instruction-Grain Lifeguards

Babak Falsafi, Evangelos Vlachos
2009
Journal Articles

Abstract

Instruction-grain lifeguards monitor executing programs at the granularity of individual instructions to quickly detect bugs and security attacks, but their fine-grain nature incurs high monitoring overheads. This article identifies three common sources of these overheads and proposes three techniques that together constitute a general-purpose hardware acceleration framework for lifeguards.

Official source

https://infoscience.epfl.ch/entities/publication/d8d7f50b-cad2-4536-a332-e9bef0c2481a

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Babak Falsafi, Evangelos Vlachos
2009
Journal Articles

Abstract

Official source

https://infoscience.epfl.ch/entities/publication/d8d7f50b-cad2-4536-a332-e9bef0c2481a

About this result

Related concepts (14)

Hardware acceleration

Hardware acceleration is the use of computer hardware designed to perform specific functions more efficiently when compared to software running on a general-purpose central processing unit (CPU). Any transformation of data that can be calculated in software running on a generic CPU can also be calculated in custom-made hardware, or in some mix of both. To perform computing tasks more quickly (or better in some other way), generally one can invest time and money in improving the software, improving the hardware, or both.

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An AI accelerator is a class of specialized hardware accelerator or computer system designed to accelerate artificial intelligence and machine learning applications, including artificial neural networks and machine vision. Typical applications include algorithms for robotics, Internet of Things, and other data-intensive or sensor-driven tasks. They are often manycore designs and generally focus on low-precision arithmetic, novel dataflow architectures or in-memory computing capability.

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General-purpose computing on graphics processing units (GPGPU, or less often GPGP) is the use of a graphics processing unit (GPU), which typically handles computation only for computer graphics, to perform computation in applications traditionally handled by the central processing unit (CPU). The use of multiple video cards in one computer, or large numbers of graphics chips, further parallelizes the already parallel nature of graphics processing.

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