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Rubén Gran-Tejero

Publications and source records attributed to Rubén Gran-Tejero.

2 recordsLinked to original sources

RRCD: Redirección de Registros Basada en Compresión de Datos para Tolerar FallosPermanentes en una GPU

The ever-increasing parallelism demand of General-Purpose Graphics Processing Unit (GPGPU) applications pushes toward larger and more energy-hungry register files in successive GPU generations. Reducing the supply voltage beyond its safe limit is an effective way to improve the energy efficiency of register files. However, at these operating voltages, the reliability of the circuit is compromised. This work aims to tolerate permanent faults from process variations in large GPU register files operating below the safe supply voltage limit. To do so, this paper proposes a microarchitectural patching technique, DC-Patch, exploiting the inherent data redundancy of applications to compress registers at run-time with neither compiler assistance nor instruction set modifications. Instead of disabling an entire faulty register file entry, DC-Patch leverages the reliable cells within a faulty entry to store compressed register values. Experimental results show that, with more than a third of faulty register entries, DC-Patch ensures a reliable operation of the register file and reduces the energy consumption by 47% with respect to a conventional register file working at nominal supply voltage. The energy savings are 21% compared to a voltage noise smoothing scheme operating at the safe supply voltage limit. These benefits are obtained with less than 2 and 6% impact on the system performance and area, respectively.

cs.AR↗

Parallelizing Workload Execution in Embedded and High-Performance Heterogeneous Systems

In this paper, we introduce a software-defined framework that enables the parallel utilization of all the programmable processing resources available in heterogeneous system-on-chip (SoC) including FPGA-based hardware accelerators and programmable CPUs. Two platforms with different architectures are considered, and a single C/C++ source code is used in both of them for the CPU and FPGA resources. Instead of simply using the hardware accelerator to offload a task from the CPU, we propose a scheduler that dynamically distributes the tasks among all the resources to fully exploit all computing devices while minimizing load unbalance. The multi-architecture study compares an ARMV7 and ARMV8 implementation with different number and type of CPU cores and also different FPGA micro-architecture and size. We measure that both platforms benefit from having the CPU cores assist FPGA execution at the same level of energy requirements.

cs.DC↗