Developing a distributed electronic health-record store for India

The DIGHT project is addressing the problem of building a scalable and highly available information store for the Electronic Health Records (EHRs) of the over one billion citizens of India

System-Level Modeling, Analysis and Code Generation: Object Recognition Case Study

International audienceOne of the most important challenges in complex embedded systems design is developing methods and tools for modeling and analyzing the behavior of application software running on multi-processor platforms. We propose a tool-supported flow for systematic and compositional construction of mixed software/hardware system models. These models are intended to represent, in an operational way, the set of timed executions of parallel application software statically mapped on a multi-processor platform. As such, system models will be used for performance analysis using simulation-based techniques as well as for code generation on specific platforms. The construction of the system model proceeds in two steps. In the first step, an abstract system model is obtained by composition and specific transformations of (1) the (untimed) model of the application software, (2) the model of the platform and (3) the mapping between them. In the second step, the abstract system model is refined into concrete system model, by including specific timing constraints for execution of the application software, according to chosen mapping on the platform. We illustrate the system model construction method and its use for performance analysis and code generation on an object recognition application provided by Hellenic Airspace Industry. This case study is build upon the HMAX models algorithm [RP99] and is looking at significant speedup factors. This paper reports results obtained on different system model configurations and used to determine the optimal implementation strategy in accordance to hardware resources

CHOICE_ WP2_D2.1_ Online mapping of Chinese and European ICT industrial associations

Funded by the 7th Framework Programme of the European Union. Grant Agreement: 61057

Instruction-set architecture exploration of VLIW ASIPs using a genetic algorithm

Genetic algorithms are commonly used for automatically solving complex design problem because exploration using genetic algorithms can consistently deliver good results when the algorithm is given a long enough run-time. However, the exploration time for problems with huge design spaces can be very long, often making exploration using a genetic algorithm practically infeasible. In this work, we present a genetic algorithm for exploring the instruction-set architecture of VLIW ASIPs and demonstrate its effectiveness by comparing it to two heuristic algorithms. We present several optimizations to the genetic algorithm configuration, and demonstrate how caching of intermediate compilation and simulation results can reduce the exploration time by an order of magnitude

One-Cycle Pruning: Pruning ConvNets Under a Tight Training Budget

Introducing sparsity in a neural network has been an efficient way to reduce its complexity while keeping its performance almost intact. Most of the time, sparsity is introduced using a three-stage pipeline: 1) train the model to convergence, 2) prune the model according to some criterion, 3) fine-tune the pruned model to recover performance. The last two steps are often performed iteratively, leading to reasonable results but also to a time-consuming and complex process. In our work, we propose to get rid of the first step of the pipeline and to combine the two other steps in a single pruning-training cycle, allowing the model to jointly learn for the optimal weights while being pruned. We do this by introducing a novel pruning schedule, named One-Cycle Pruning, which starts pruning from the beginning of the training, and until its very end. Adopting such a schedule not only leads to better performing pruned models but also drastically reduces the training budget required to prune a model. Experiments are conducted on a variety of architectures (VGG-16 and ResNet-18) and datasets (CIFAR-10, CIFAR-100 and Caltech-101), and for relatively high sparsity values (80%, 90%, 95% of weights removed). Our results show that One-Cycle Pruning consistently outperforms commonly used pruning schedules such as One-Shot Pruning, Iterative Pruning and Automated Gradual Pruning, on a fixed training budget.Comment: Accepted at Sparsity in Neural Networks (SNN 2021

Data dependent energy modelling for worst case energy consumption analysis

Safely meeting Worst Case Energy Consumption (WCEC) criteria requires accurate energy modeling of software. We investigate the impact of instruction operand values upon energy consumption in cacheless embedded processors. Existing instruction-level energy models typically use measurements from random input data, providing estimates unsuitable for safe WCEC analysis. We examine probabilistic energy distributions of instructions and propose a model for composing instruction sequences using distributions, enabling WCEC analysis on program basic blocks. The worst case is predicted with statistical analysis. Further, we verify that the energy of embedded benchmarks can be characterised as a distribution, and compare our proposed technique with other methods of estimating energy consumption

CRI planning and scheduling for space

Computer Resources International (CRI) has many years of experience in developing space planning and scheduling systems for the European Space Agency. Activities range from AIT/AIV planning over mission planning to research in on-board autonomy using advanced planning and scheduling technologies in conjunction with model based diagnostics. This article presents four projects carried out for ESA by CRI with various subcontractors: (1) DI, Distributed Intelligence for Ground/Space Systems is an on-going research project; (2) GMPT, Generic Mission Planning Toolset, a feasibility study concluded in 1993; (3) OPTIMUM-AIV, Open Planning Tool for AIV, development of a knowledge based AIV planning and scheduling tool ended in 1992; and (4) PlanERS-1, development of an AI and knowledge-based mission planning prototype for the ERS-1 earth observation spacecraft ended in 1991