Safety-critical applications require well-defined and documented timing behavior. These requirements shape the design and implementation of a timing analyzer based on a formal Instruction-Set Architecture (ISA) semantics and formal micro-architecture models. In this paper we present the key elements of such a timing analyzer and how to systematically combine the formal components to address timing properties such as evaluating memory interferences. We also report preliminary experiments of memory interference analysis of multi-threaded applications in a multicore context.

We propose a flexible light-weight and parametric NoC model designed for fast performance estimation at early design stages. Our NoC model combines the benefits of both analytical and simulation-based NoC models. Our NoC features an abstract router model whose buffers are updated at runtime with information about the actual traffic. This traffic information is fed to a closed-form expression that computes packet latency and that accounts for network contention at a router basis. We evaluated our hybrid NoC model in terms of estimation accuracy and simulation speed. We compared the simulation results to the ones obtained with a cycle accurate NoC simulator called Garnet. Our NoC model achieves less than 17% error in average network latency estimation and attains up to 14 × speedup for a 8 × 8 mesh.

In this paper, we propose an analytical approach to study the impact of floating point (FLP) precision variation on the square root operation, in terms of computational accuracy and performance gain. We estimate the round-off error resulting from reduced precision. We also inspect the Newton Raphson algorithm used to approximate the square root in order to bound the error caused by algorithmic deviation. Consequently, the implementation of the square root can be optimized by fittingly adjusting its number of iterations with respect to any given FLP precision specification, without the need for long simulation times. We evaluate our error analysis of the square root operation as part of approximating a classic data clustering algorithm known as K-means, for the purpose of reducing its energy footprint. We compare the resulting inexact K-means to its exact counterpart, in the context of color quantization, in terms of energy gain and quality of the output. The experimental results show that energy savings could be achieved without penalizing the quality of the output (e.g., up to 41.87% of energy gain for an output quality, measured using structural similarity, within a range of [0.95,1]).