BrainFrame: A node-level heterogeneous accelerator platform for neuron simulations

Objective. The advent of high-performance computing (HPC) in recent years has led to its increasing use in brain studies through computational models. The scale and complexity of such models are constantly increasing, leading to challenging computational requirements. Even though modern HPC platforms can often deal with such challenges, the vast diversity of the modeling field does not permit for a homogeneous acceleration platform to effectively address the complete array of modeling requirements. Approach. In this paper we propose and build BrainFrame, a heterogeneous acceleration platform that incorporates three distinct acceleration technologies, an Intel Xeon-Phi CPU

Fast Estimations of Failure Probability Over Long Time Spans

none9siShrinking of device dimensions has undoubtedly enabled the very large scale integration of transistors on electronic chips. However, it has also brought to surface time-zero and time-dependent variation phenomena that degrade system's performance and threaten functional operation. Hence, the need to capture and describe these mechanisms, as well as effectively model their impact is crucial. To this extent, we follow existing models and propose a complete framework that evaluates failure probability of electronic components. To assess our framework, a case-study of packet-switched Network on Chip (NoC) routers is presented, studying the failure probability of its SRAM buffers.noneNoltsis, Michail; Englezakis, Panayiotis; Maragkoudaki, Eleni; Nicopoulos, Chrysostomos; Rodopoulos, Dimitrios; Catthoor, Francky; Sazeides, Yiannakis; Zoni, Davide; Soudris, DimitriosNoltsis, Michail; Englezakis, Panayiotis; Maragkoudaki, Eleni; Nicopoulos, Chrysostomos; Rodopoulos, Dimitrios; Catthoor, Francky; Sazeides, Yiannakis; Zoni, Davide; Soudris, Dimitrio