Guppy: Process-Oriented Programming on Embedded Devices

Guppy is a new and experimental process-oriented programming language, taking much inspiration (and some code-base) from the existing occam-pi language. This paper reports on a variety of aspects related to this, specifically language, compiler and run-time system development, enabling Guppy programs to run on desktop and embedded systems. A native code-generation approach is taken, using C as the intermediate language, and with stack-space requirements determined at compile-time

A new Java Thread model for concurrent programming of real-time systems

The Java ™ Virtual Machine (JVM) provides a high degree of platform independence, but being an interpreter, Java has a poor system performance. New compiler techniques and Java processors will gradually improve the performance of Java, but despite these developments, Java is still far from real-time. We propose the Communicating Java Threads (CJT) model, which eliminates several shortcomings, such as Java's non-deterministic behavior, Java's monitor weakness, and lack of reactiveness for real-time and embedded systems. CJT is based on CSP providing channels, composition constructs, and scheduling of processes. The CJT Java class library, which provides all necessary classes on top of Java, is readily available to interested users. The main power of the method is that it integrates these features in a natural way and in no way conflicts with other paradigms, such as PersonalJava ™, EmbeddedJava ™, JavaBeans ™, CORBA, and PVM. All activities, reported in this paper, are carried out as part of the JavaPP project

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

Benchmarking of Embedded Object Detection in Optical and RADAR Scenes

A portable, real-time vital sign estimation protoype is developed using neural network- based localization, multi-object tracking, and embedded processing optimizations. The system estimates heart and respiration rates of multiple subjects using directional of arrival techniques on RADAR data. This system is useful in many civilian and military applications including search and rescue. The primary contribution from this work is the implementation and benchmarking of neural networks for real time detection and localization on various systems including the testing of eight neural networks on a discrete GPU and Jetson Xavier devices. Mean average precision (mAP) and inference speed benchmarks were performed. We have shown fast and accurate detection and tracking using synthetic and real RADAR data. Another major contribution is the quantification of the relationship between neural network mAP performance and data augmentations. As an example, we focused on image and video compression methods, such as JPEG, WebP, H264, and H265. The results show WebP at a quantization level of 50 and H265 at a constant rate factor of 30 provide the best balance between compression and acceptable mAP. Other minor contributions are achieved in enhancing the functionality of the real-time prototype system. This includes the implementation and benchmarking of neural network op- timizations, such as quantization and pruning. Furthermore, an appearance-based synthetic RADAR and real RADAR datasets are developed. The latter contains simultaneous optical and RADAR data capture and cross-modal labels. Finally, multi-object tracking methods are benchmarked and a support vector machine is utilized for cross-modal association. In summary, the implementation, benchmarking, and optimization of methods for detection and tracking helped create a real-time vital sign system on a low-profile embedded device. Additionally, this work established a relationship between compression methods and different neural networks for optimal file compression and network performance. Finally, methods for RADAR and optical data collection and cross-modal association are implemented

Life of occam-Pi

This paper considers some questions prompted by a brief review of the history of computing. Why is programming so hard? Why is concurrency considered an “advanced” subject? What’s the matter with Objects? Where did all the Maths go? In searching for answers, the paper looks at some concerns over fundamental ideas within object orientation (as represented by modern programming languages), before focussing on the concurrency model of communicating processes and its particular expression in the occam family of languages. In that focus, it looks at the history of occam, its underlying philosophy (Ockham’s Razor), its semantic foundation on Hoare’s CSP, its principles of process oriented design and its development over almost three decades into occam-? (which blends in the concurrency dynamics of Milner’s ?-calculus). Also presented will be an urgent need for rationalisation – occam-? is an experiment that has demonstrated significant results, but now needs time to be spent on careful review and implementing the conclusions of that review. Finally, the future is considered. In particular, is there a future

Beyond the Threaded Programming Model on Real-Time Operating Systems

The use of a real-time operating system (RTOS) raises the abstraction level for embedded systems design when compared to traditional bare-metal programming, resulting in simpler and more reusable application code. Modern RTOSes for resource-constrained platforms, like Zephyr and FreeRTOS, also offer threading support, but this kind of shared memory concurrency is a poor fit for expressing the reactive and interactive behaviors that are common in embedded systems. To address this, alternative concurrency models like the actor model or communicating sequential processes have been proposed. While those alternatives enable reactive design patterns, they fail to deliver determinism and do not address timing. This makes it difficult to verify that implemented behavior is as intended and impossible to specify timing constraints in a portable way. This makes it hard to create reusable library components out of common embedded design patterns, forcing developers to keep reinventing the wheel for each application and each platform. In this paper, we introduce the embedded target of Lingua Franca (LF) as a means to move beyond the threaded programming model provided by RTOSes and improve the state of the art in embedded programming. LF is based on the reactor model of computation, which is reactive, deterministic, and timed, providing a means to express concurrency and timing in a platform-independent way. We compare the performance of LF versus threaded C code - both running on Zephyr - in terms of response time, timing precision, throughput, and memory footprint

SpaceCube: A NASA Family of Reconfigurable Hybrid On-Board Science Data Processors

SpaceCube is a family of Field Programmable Gate Array (FPGA) based on-board science-data processing systems developed at NASA Goddard Space Flight Center. This presentation provides an overview to the Future In-Space Operations Telecon Working Group

Neuraghe: Exploiting CPU-FPGA synergies for efficient and flexible CNN inference acceleration on zynQ SoCs

Deep convolutional neural networks (CNNs) obtain outstanding results in tasks that require human-level understanding of data, like image or speech recognition. However, their computational load is significant, motivating the development of CNN-specialized accelerators. This work presents NEURAghe, a flexible and efficient hardware/software solution for the acceleration of CNNs on Zynq SoCs. NEURAghe leverages the synergistic usage of Zynq ARM cores and of a powerful and flexible Convolution-Specific Processor deployed on the reconfigurable logic. The Convolution-Specific Processor embeds both a convolution engine and a programmable soft core, releasing the ARM processors from most of the supervision duties and allowing the accelerator to be controlled by software at an ultra-fine granularity. This methodology opens the way for cooperative heterogeneous computing: While the accelerator takes care of the bulk of the CNN workload, the ARM cores can seamlessly execute hard-to-accelerate parts of the computational graph, taking advantage of the NEON vector engines to further speed up computation. Through the companion NeuDNN SW stack, NEURAghe supports end-to-end CNN-based classification with a peak performance of 169GOps/s and an energy efficiency of 17GOps/W. Thanks to our heterogeneous computing model, our platform improves upon the state-of-the-art, achieving a frame rate of 5.5 frames per second (fps) on the end-to-end execution of VGG-16 and 6.6fps on ResNet-18

Survey: Benefits of integrating both wireless sensors networks and cloud computing infrastructure

Cloud computing has the capabilities of powerful processing and scalable storage with the ability of offline and online data analysis and mining of the collected sensed data from body areas networks. Cloud computing can be considered as the main enabler for modern manufacturing industries. Cloud computing can efficiently serve key areas of manufacturing by aspects of the pay-as-you-go business model, scaling up and down production according to certain demands, more customized solutions, and flexible deployments. In cloud manufacturing, the distributed sensors and resources can be managed in centralized architecture that allows cloud users to request more specific product design, testing at all the stages of the product. This study covers the main points of Integrating Both Wireless Sensors Networks and Cloud Computing Infrastructure and gives a view of the various advantage and disadvantages of methods in integration