A Survey of Techniques For Improving Energy Efficiency in Embedded Computing Systems

Recent technological advances have greatly improved the performance and features of embedded systems. With the number of just mobile devices now reaching nearly equal to the population of earth, embedded systems have truly become ubiquitous. These trends, however, have also made the task of managing their power consumption extremely challenging. In recent years, several techniques have been proposed to address this issue. In this paper, we survey the techniques for managing power consumption of embedded systems. We discuss the need of power management and provide a classification of the techniques on several important parameters to highlight their similarities and differences. This paper is intended to help the researchers and application-developers in gaining insights into the working of power management techniques and designing even more efficient high-performance embedded systems of tomorrow

Efficient Implementation on Low-Cost SoC-FPGAs of TLSv1.2 Protocol with ECC_AES Support for Secure IoT Coordinators

Security management for IoT applications is a critical research field, especially when taking into account the performance variation over the very different IoT devices. In this paper, we present high-performance client/server coordinators on low-cost SoC-FPGA devices for secure IoT data collection. Security is ensured by using the Transport Layer Security (TLS) protocol based on the TLS_ECDHE_ECDSA_WITH_AES_128_CBC_SHA256 cipher suite. The hardware architecture of the proposed coordinators is based on SW/HW co-design, implementing within the hardware accelerator core Elliptic Curve Scalar Multiplication (ECSM), which is the core operation of Elliptic Curve Cryptosystems (ECC). Meanwhile, the control of the overall TLS scheme is performed in software by an ARM Cortex-A9 microprocessor. In fact, the implementation of the ECC accelerator core around an ARM microprocessor allows not only the improvement of ECSM execution but also the performance enhancement of the overall cryptosystem. The integration of the ARM processor enables to exploit the possibility of embedded Linux features for high system flexibility. As a result, the proposed ECC accelerator requires limited area, with only 3395 LUTs on the Zynq device used to perform high-speed, 233-bit ECSMs in 413 µs, with a 50 MHz clock. Moreover, the generation of a 384-bit TLS handshake secret key between client and server coordinators requires 67.5 ms on a low cost Zynq 7Z007S device

Modeling and visualizing networked multi-core embedded software energy consumption

In this report we present a network-level multi-core energy model and a software development process workflow that allows software developers to estimate the energy consumption of multi-core embedded programs. This work focuses on a high performance, cache-less and timing predictable embedded processor architecture, XS1. Prior modelling work is improved to increase accuracy, then extended to be parametric with respect to voltage and frequency scaling (VFS) and then integrated into a larger scale model of a network of interconnected cores. The modelling is supported by enhancements to an open source instruction set simulator to provide the first network timing aware simulations of the target architecture. Simulation based modelling techniques are combined with methods of results presentation to demonstrate how such work can be integrated into a software developer's workflow, enabling the developer to make informed, energy aware coding decisions. A set of single-, multi-threaded and multi-core benchmarks are used to exercise and evaluate the models and provide use case examples for how results can be presented and interpreted. The models all yield accuracy within an average +/-5 % error margin

Overview of Swallow --- A Scalable 480-core System for Investigating the Performance and Energy Efficiency of Many-core Applications and Operating Systems

We present Swallow, a scalable many-core architecture, with a current configuration of 480 x 32-bit processors. Swallow is an open-source architecture, designed from the ground up to deliver scalable increases in usable computational power to allow experimentation with many-core applications and the operating systems that support them. Scalability is enabled by the creation of a tile-able system with a low-latency interconnect, featuring an attractive communication-to-computation ratio and the use of a distributed memory configuration. We analyse the energy and computational and communication performances of Swallow. The system provides 240GIPS with each core consuming 71--193mW, dependent on workload. Power consumption per instruction is lower than almost all systems of comparable scale. We also show how the use of a distributed operating system (nOS) allows the easy creation of scalable software to exploit Swallow's potential. Finally, we show two use case studies: modelling neurons and the overlay of shared memory on a distributed memory system.Comment: An open source release of the Swallow system design and code will follow and references to these will be added at a later dat

An IoT Endpoint System-on-Chip for Secure and Energy-Efficient Near-Sensor Analytics

Near-sensor data analytics is a promising direction for IoT endpoints, as it minimizes energy spent on communication and reduces network load - but it also poses security concerns, as valuable data is stored or sent over the network at various stages of the analytics pipeline. Using encryption to protect sensitive data at the boundary of the on-chip analytics engine is a way to address data security issues. To cope with the combined workload of analytics and encryption in a tight power envelope, we propose Fulmine, a System-on-Chip based on a tightly-coupled multi-core cluster augmented with specialized blocks for compute-intensive data processing and encryption functions, supporting software programmability for regular computing tasks. The Fulmine SoC, fabricated in 65nm technology, consumes less than 20mW on average at 0.8V achieving an efficiency of up to 70pJ/B in encryption, 50pJ/px in convolution, or up to 25MIPS/mW in software. As a strong argument for real-life flexible application of our platform, we show experimental results for three secure analytics use cases: secure autonomous aerial surveillance with a state-of-the-art deep CNN consuming 3.16pJ per equivalent RISC op; local CNN-based face detection with secured remote recognition in 5.74pJ/op; and seizure detection with encrypted data collection from EEG within 12.7pJ/op.Comment: 15 pages, 12 figures, accepted for publication to the IEEE Transactions on Circuits and Systems - I: Regular Paper