Toolflows for Mapping Convolutional Neural Networks on FPGAs: A Survey and Future Directions

In the past decade, Convolutional Neural Networks (CNNs) have demonstrated state-of-the-art performance in various Artificial Intelligence tasks. To accelerate the experimentation and development of CNNs, several software frameworks have been released, primarily targeting power-hungry CPUs and GPUs. In this context, reconfigurable hardware in the form of FPGAs constitutes a potential alternative platform that can be integrated in the existing deep learning ecosystem to provide a tunable balance between performance, power consumption and programmability. In this paper, a survey of the existing CNN-to-FPGA toolflows is presented, comprising a comparative study of their key characteristics which include the supported applications, architectural choices, design space exploration methods and achieved performance. Moreover, major challenges and objectives introduced by the latest trends in CNN algorithmic research are identified and presented. Finally, a uniform evaluation methodology is proposed, aiming at the comprehensive, complete and in-depth evaluation of CNN-to-FPGA toolflows.Comment: Accepted for publication at the ACM Computing Surveys (CSUR) journal, 201

Approximate FPGA-based LSTMs under Computation Time Constraints

Recurrent Neural Networks and in particular Long Short-Term Memory (LSTM) networks have demonstrated state-of-the-art accuracy in several emerging Artificial Intelligence tasks. However, the models are becoming increasingly demanding in terms of computational and memory load. Emerging latency-sensitive applications including mobile robots and autonomous vehicles often operate under stringent computation time constraints. In this paper, we address the challenge of deploying computationally demanding LSTMs at a constrained time budget by introducing an approximate computing scheme that combines iterative low-rank compression and pruning, along with a novel FPGA-based LSTM architecture. Combined in an end-to-end framework, the approximation method's parameters are optimised and the architecture is configured to address the problem of high-performance LSTM execution in time-constrained applications. Quantitative evaluation on a real-life image captioning application indicates that the proposed methods required up to 6.5x less time to achieve the same application-level accuracy compared to a baseline method, while achieving an average of 25x higher accuracy under the same computation time constraints.Comment: Accepted at the 14th International Symposium in Applied Reconfigurable Computing (ARC) 201

Multi-Exit Semantic Segmentation Networks

Semantic segmentation arises as the backbone of many vision systems, spanning from self-driving cars and robot navigation to augmented reality and teleconferencing. Frequently operating under stringent latency constraints within a limited resource envelope, optimising for efficient execution becomes important. At the same time, the heterogeneous capabilities of the target platforms and the diverse constraints of different applications require the design and training of multiple target-specific segmentation models, leading to excessive maintenance costs. To this end, we propose a framework for converting state-of-the-art segmentation CNNs to Multi-Exit Semantic Segmentation (MESS) networks: specially trained models that employ parametrised early exits along their depth to i) dynamically save computation during inference on easier samples and ii) save training and maintenance cost by offering a post-training customisable speed-accuracy trade-off. Designing and training such networks naively can hurt performance. Thus, we propose a novel two-staged training scheme for multi-exit networks. Furthermore, the parametrisation of MESS enables co-optimising the number, placement and architecture of the attached segmentation heads along with the exit policy, upon deployment via exhaustive search in <1 GPUh. This allows MESS to rapidly adapt to the device capabilities and application requirements for each target use-case, offering a train-once-deploy-everywhere solution. MESS variants achieve latency gains of up to 2.83x with the same accuracy, or 5.33 pp higher accuracy for the same computational budget, compared to the original backbone network. Lastly, MESS delivers orders of magnitude faster architectural customisation, compared to state-of-the-art techniques.Comment: (Extended version) Accepted at ECCV 202

The need for an online collection of traditional african food habits

Amongst the difficulties facing the indigenous people of Africa today is the deleterious shift from traditional food habits to the processed and packaged food products of western-owned corporations. This nutrition transition has been implicated in the rise of non-communicable diseases (NCDs) throughout Africa. The purpose of the present investigation was to determine whether there is a current need to document traditional African food habits via an online collection in an attempt to stimulate further research in this area and potentially improve the health status of indigenous Africans threatened by the nutrition transition. A systematic  search was performed to assess possible gaps in online collections focused on traditional African food habits. A questionnaire was administered to opinion leaders in the nutritional sciences at the 18th International Congress of Nutrition (ICN) in Durban, South Africa, September 2005, to determine the level of awareness of the importance of traditional African food habits within the context of the nutrition transition, and to determine the support among this cohort for an online collection of traditional African food habits. Our systematic review resulted in nine collections being identified. None of these collections were specifically  designed to raise  awareness of traditional African food habits however. Findings from the survey revealed that 86% of our cohort agreed that Africa is currently undergoing a  nutrition transition. Nearly 80% believed that knowledge of traditional African food habits is being lost. Indigenous African interviewees noted reduced consumption of sorghum and millet and an increased   consumption of wheat and rice within their region of origin. Approximately 82% believed that there was currently a gap in online collections focused on presenting information on traditional African food habits. Ninety-two percent of the cohort indicated their preparedness to make use of a novel, online collection of data on traditional African food habits. The findings revealed a critical need to collate and present data on traditional African food habits via a novel, online collection that could be used to stimulate education and research of food habits and their health implications, to provide a well-rounded forum in which such information is presented and shared.Key words: Africa, traditional foods, wild species, dietary practices, information networks and database

Healthier and Independent Living of the Elderly: Interoperability in a Cross-Project Pilot

The ageing of the population creates new heterogeneous challenges for age-friendly living. The progressive decline in physical and cognitive skills tends to prevent elderly people from performing basic instrumental activities of daily living and there is a growing interest in technology for aging support. Digital health today can be exercised by anyone owning a smartphone and parameters such as heart rate, step counts, calorie intake, sleep quality, can be collected and used not only to monitor and improve the individual’s health condition but also to prevent illnesses. However, for the benefits of e-health to take place, digital health data, either Electronic Health Records (EHR) or sensor data from the IoMT, must be shared, maintaining privacy and security requirements but unlocking the potential for research an innovation throughout EU. This paper demonstrates the added value of such interoperability requirements, and a form of accomplishing them through a cross-project pilot

Further oblique-incidence ionospheric soundings over Central Europe to test nowcasting and long term prediction models

After a first oblique-incidence ionospheric sounding campaign over Central Europe performed during the period 2003-2004 over the radio links between Inskip (UK, 53.5° N, 2.5° W) and Rome (Italy, 41.8 N, 12.5E) and between Inskip and Chania (Crete, 35.7° N, 24.0° E), new and more extensive analysis of systematic MUF measurements from January 2005 to December 2006 have been performed. MUF measurements collected during moderately disturbed days (17 ≤ Ap ≤ 32), disturbed days (32 50), have been used to test the long term prediction models (ASAPS, ICEPAC and SIRM&LKW), and the now casting models (SIRMUP&LKW and ISWIRM&LKW). The performances of the different prediction methods in terms of r.m.s are shown for selected range of geomagnetic activity and for each season.Submitted3.9. Fisica della magnetosfera, ionosfera e meteorologia spazialeN/A or not JCRope

Near-Earth space plasma modelling and forecasting

In the frame of the European COST 296 project (Mitigation of Ionospheric Effects on Radio Systems, MIERS)in the Working Package 1.3, new ionospheric models, prediction and forecasting methods and programs as well as ionospheric imaging techniques have been developed. They include (i) topside ionosphere and meso-scale irregularity models, (ii) improved forecasting methods for real time forecasting and for prediction of foF2, M(3000)F2, MUF and TECs, including the use of new techniques such as Neurofuzzy, Nearest Neighbour, Cascade Modelling and Genetic Programming and (iii) improved dynamic high latitude ionosphere models through tomographic imaging and model validation. The success of the prediction algorithms and their improvement over existing methods has been demonstrated by comparing predictions with later real data. The collaboration between different European partners (including interchange of data) has played a significant part in the development and validation of these new prediction and forecasting methods, programs and algorithms which can be applied to a variety of practical applications leading to improved mitigation of ionosphereic and space weather effects.Published255-2713.9. Fisica della magnetosfera, ionosfera e meteorologia spazialeJCR Journalope

Near-Earth space plasma modelling and forecasting

In the frame of the European COST 296 project (Mitigation of Ionospheric Effects on Radio Systems, MIERS)in the Working Package 1.3, new ionospheric models, prediction and forecasting methods and programs as well as ionospheric imaging techniques have been developed. They include (i) topside ionosphere and meso-scale irregularity models, (ii) improved forecasting methods for real time forecasting and for prediction of foF2, M(3000)F2, MUF and TECs, including the use of new techniques such as Neurofuzzy, Nearest Neighbour, Cascade Modelling and Genetic Programming and (iii) improved dynamic high latitude ionosphere models through tomographic imaging and model validation. The success of the prediction algorithms and their improvement over existing methods has been demonstrated by comparing predictions with later real data. The collaboration between different European partners (including interchange of data) has played a significant part in the development and validation of these new prediction and forecasting methods, programs and algorithms which can be applied to a variety of practical applications leading to improved mitigation of ionosphereic and space weather effects