Automatic vector generation guided by a functional metric

Verification is still the bottleneck of the complex digital system design process. Formal techniques have advanced in their capacity to handle more complex descriptions, but they still suffer from problems of memory or time explosion. Simulation-based techniques handle descriptions of any size or complexity, but the efficiency of these techniques is reduced with the increase in the system complexity because of the exponential increase in the number of simulation tests necessary to maintain the coverage. Semi-formal techniques combine the advantages of simulation and formal techniques as they increase the efficiency of simulation-based verification. In this area, several research works have introduced techniques that automate the generation of vectors driven by traditional coverage metrics. However, these techniques do not ensure the detection of 100% of faults. This paper presents a novel technique for the generation of vectors. A major benefit of the technique is the more efficient generation of test-benches than when using techniques based on structural metrics. The technique introduced is more efficient since it relies on a novel coverage metric, which is more directly correlated to functional faults than structural coverage metrics (line, branch, etc.). The proposed coverage metric is based on an abstraction of the system as a set of polynomials where all system behaviours are described by a set of coefficients. By assuming a finite precision of coefficients and a maximum degree of polynomials, all the system behaviors, including both the correct and the incorrect ones, can be modeled. This technique applies mathematical theories (computer algebra and number theory) to calculate the coverage and to generate vectors which maximize coverage. Moreover, in this work, a tool which implements the technique has been developed. This tool takes a C-based system description and provides the coverage and the generated vectors as output

Design space exploration in heterogeneous platforms using OpenMP

In the fields of high performance computing (HPC) and embedded systems, the current trend is to employ heterogeneous platforms which integrate general purpose CPUs with specialized accelerators such as GPUs and FPGAs. Programming these architectures to approach their theoretical performance limits is a complex issue. In this article, we present a design methodology targeting heterogeneous platforms which combines a novel dynamic offloading mechanism for OpenMP and a scheduling strategy for assigning tasks to accelerator devices. The current OpenMP offloading model depends on the compiler supporting each target device, with many architectures still unsupported by the most popular compilers, such as GCC and Clang. In our approach, the software and/or hardware design flows for programming the accelerators are dissociated from the host OpenMP compiler and the device-specific implementations are dynamically loaded at runtime. Moreover, the assignment of tasks to computing resources is dynamically evaluated at runtime, with the aim of maximizing performance when using the available resources. The proposed methodology has been applied to a video processing system as a test case. The results demonstrate the flexibility of the proposal by exploiting different heterogeneous platforms and design particularities of devices, leading to a significant performance improvement.This work has been funded by FEDER/Ministerio de Ciencia, Innovación y Universidades – Agencia Estatal de Investigacion/TEC2017-86722-C4-3-R, also under the FitOptiVis Project (ECSEL2017-1-737451), which is funded by the EU (H2020) and Ministerio de Ciencia, Innovación y Universidades

Pre-silicon FEC decoding verification on SoC FPGAs

Forward error correction (FEC) decoding hardware modules are challenging to verify at pre-silicon stage, when they are usually described at register-transfer (RT)/logic level with a hardware description language (HDL). They tend to hide faults due to their inherent tendency to correct errors and the required simulations with a massive insertion of inputs are too slow. In this work, two verification techniques based on FPGA-prototyping are applied in order to complement the mentioned simulations: golden model vs implementation matching with thousands of random codewords and codeword/bit error rate (CER/BER) curve computation. For this purpose, a system on chip (SoC) field-programmable gate array (FPGA) is used, implementing in the programmable hardware part several replicas of the decoder (exploiting the parallel capabilities of hardware) and managing the verification by parallel programming the software part of the SoC (exploiting the presence of multiple processing cores). The presented approach allows a seamless integration with high-level models, does not need expensive testing/emulation platforms and obtains the results in a reasonable amount of time.This work has been supported by Project TEC2017-86722-C4-3-R, funded by Spanish MICINN/AEI