Fernando Akira Endo

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Energy consumption is the major factor limiting performance in embedded systems. In addition, in the next generations of ICs, heat or energy constraints will not allow to power all transistors simultaneously. Heterogeneous multicore systems represent a possible solution to this problem: the diversity of cores provides energy and performance trade-offs. (More)
Heterogeneous multicore systems have gained momentum, specially for embedded applications, thanks to the performance and energy consumption trade-offs provided by inorder and out-of-order cores. Micro-architectural simulation models the behavior of pipeline structures and caches with configurable parameters. This level of abstraction is well known for being(More)
The processing applications that are now being used in mobile and embedded platforms require at the same time a fair amount of processing power and a high level of flexibility, due to the nature of the data to process. In this context we propose a lightweight code generation technique that is able to perform data dependent optimizations at run-time for(More)
This paper proposes an online auto-tuning approach for computing kernels. Differently from existing online auto-tuners, which regenerate code with long compilation chains from the source to the binary code, our approach consists on deploying auto-tuning directly at the level of machine code generation. This allows auto-tuning to pay off in very(More)
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