Sub-pJ per operation scalable computing: The PULP experience

none1noUltra-low power operation and extreme energy efficiency are strong requirements for a number of high-growth Internet of-Things (IoT) applications requiring near-sensor processing. A promising approach to achieve major energy efficiency improvements is near-threshold computing. However, frequency degradation due to aggressive voltage scaling may not be acceptable for performance-constrained applications. The PULP platform leverages multi-core parallelism with explicitly-managed shared L1 memory to overcome performance degradation at low voltage, while maintaining the flexibility and programmability typical of instruction processors. PULP supports OpenMP, OpenCL, and OpenVX parallel programming with hardware support for energy efficient synchronization. Multiple silicon implementations of PULP have been taped out and achieve hundreds of GOPS/W on video, audio, inertial sensor data processing and classification, within power envelopes of a few milliwatts. PULP hardware and software are open-source, with the goal of supporting and boosting an innovation ecosystem focusing on ULP computing for the IoT.openRossi, DavideRossi, David

Heterogeneous Architectures For Parallel Acceleration

To enable a new generation of digital computing applications, the greatest challenge is to provide a better level of energy efficiency (intended as the performance that a system can provide within a certain power budget) without giving up a systems's flexibility. This constraint applies to digital system across all scales, starting from ultra-low power implanted devices up to datacenters for high-performance computing and for the "cloud". In this thesis, we show that architectural heterogeneity is the key to provide this efficiency and to respond to many of the challenges of tomorrow's computer architecture - and at the same time we show methodologies to introduce it with little or no loss in terms of flexibility. In particular, we show that heterogeneity can be employed to tackle the "walls" that impede further development of new computing applications: the utilization wall, i.e. the impossibility to keep all transistors on in deeply integrated chips, and the "data deluge", i.e. the amount of data to be processed that is scaling up much faster than the computing performance and efficiency. We introduce a methodology to improve heterogeneous design exploration of tightly coupled clusters; moreover we propose a fractal heterogeneity architecture that is a parallel accelerator for low-power sensor nodes, and is itself internally heterogeneous thanks to an heterogeneous coprocessor for brain-inspired computing. This platform, which is silicon-proven, can lead to more than 100x improvement in terms of energy efficiency with respect to typical computing nodes used within the same domain, enabling the application of complex algorithms, vastly more performance-hungry than the current state-of-the-art in the ULP computing domain

High performance and energy-efficient instruction cache design and optimisation for ultra-low-power multi-core clusters

High Energy efficiency and high performance are the key regiments for Internet of Things (IoT) end-nodes. Exploiting cluster of multiple programmable processors has recently emerged as a suitable solution to address this challenge. However, one of the main bottlenecks for multi-core architectures is the instruction cache. While private caches fall into data replication and wasting area, fully shared caches lack scalability and form a bottleneck for the operating frequency. Hence we propose a hybrid solution where a larger shared cache (L1.5) is shared by multiple cores connected through a low-latency interconnect to small private caches (L1). However, it is still limited by large capacity miss with a small L1. Thus, we propose a sequential prefetch from L1 to L1.5 to improve the performance with little area overhead. Moreover, to cut the critical path for better timing, we optimized the core instruction fetch stage with non-blocking transfer by adopting a 4 x 32-bit ring buffer FIFO and adding a pipeline for the conditional branch. We present a detailed comparison of different instruction cache architectures' performance and energy efficiency recently proposed for Parallel Ultra-Low-Power clusters. On average, when executing a set of real-life IoT applications, our two-level cache improves the performance by up to 20% and loses 7% energy efficiency with respect to the private cache. Compared to a shared cache system, it improves performance by up to 17% and keeps the same energy efficiency. In the end, up to 20% timing (maximum frequency) improvement and software control enable the two-level instruction cache with prefetch adapt to various battery-powered usage cases to balance high performance and energy efficiency

Optimized Biosignals Processing Algorithms for New Designs of Human Machine Interfaces on Parallel Ultra-Low Power Architectures

The aim of this dissertation is to explore Human Machine Interfaces (HMIs) in a variety of biomedical scenarios. The research addresses typical challenges in wearable and implantable devices for diagnostic, monitoring, and prosthetic purposes, suggesting a methodology for tailoring such applications to cutting edge embedded architectures. The main challenge is the enhancement of high-level applications, also introducing Machine Learning (ML) algorithms, using parallel programming and specialized hardware to improve the performance. The majority of these algorithms are computationally intensive, posing significant challenges for the deployment on embedded devices, which have several limitations in term of memory size, maximum operative frequency, and battery duration. The proposed solutions take advantage of a Parallel Ultra-Low Power (PULP) architecture, enhancing the elaboration on specific target architectures, heavily optimizing the execution, exploiting software and hardware resources. The thesis starts by describing a methodology that can be considered a guideline to efficiently implement algorithms on embedded architectures. This is followed by several case studies in the biomedical field, starting with the analysis of a Hand Gesture Recognition, based on the Hyperdimensional Computing algorithm, which allows performing a fast on-chip re-training, and a comparison with the state-of-the-art Support Vector Machine (SVM); then a Brain Machine Interface (BCI) to detect the respond of the brain to a visual stimulus follows in the manuscript. Furthermore, a seizure detection application is also presented, exploring different solutions for the dimensionality reduction of the input signals. The last part is dedicated to an exploration of typical modules for the development of optimized ECG-based applications

Temperature Variation Aware Energy Optimization in Heterogeneous MPSoCs

Thermal effects are rapidly gaining importance in nanometer heterogeneous integrated systems. Increased power density, coupled with spatio-temporal variability of chip workload, cause lateral and vertical temperature non-uniformities (variations) in the chip structure. The assumption of an uniform temperature for a large circuit leads to inaccurate determination of key design parameters. To improve design quality, we need precise estimation of temperature at detailed spatial resolution which is very computationally intensive. Consequently, thermal analysis of the designs needs to be done at multiple levels of granularity. To further investigate the flow of chip/package thermal analysis we exploit the Intel Single Chip Cloud Computer (SCC) and propose a methodology for calibration of SCC on-die temperature sensors. We also develop an infrastructure for online monitoring of SCC temperature sensor readings and SCC power consumption. Having the thermal simulation tool in hand, we propose MiMAPT, an approach for analyzing delay, power and temperature in digital integrated circuits. MiMAPT integrates seamlessly into industrial Front-end and Back-end chip design flows. It accounts for temperature non-uniformities and self-heating while performing analysis. Furthermore, we extend the temperature variation aware analysis of designs to 3D MPSoCs with Wide-I/O DRAM. We improve the DRAM refresh power by considering the lateral and vertical temperature variations in the 3D structure and adapting the per-DRAM-bank refresh period accordingly. We develop an advanced virtual platform which models the performance, power, and thermal behavior of a 3D-integrated MPSoC with Wide-I/O DRAMs in detail. Moving towards real-world multi-core heterogeneous SoC designs, a reconfigurable heterogeneous platform (ZYNQ) is exploited to further study the performance and energy efficiency of various CPU-accelerator data sharing methods in heterogeneous hardware architectures. A complete hardware accelerator featuring clusters of OpenRISC CPUs, with dynamic address remapping capability is built and verified on a real hardware

Integrated Programmable-Array accelerator to design heterogeneous ultra-low power manycore architectures

There is an ever-increasing demand for energy efficiency (EE) in rapidly evolving Internet-of-Things end nodes. This pushes researchers and engineers to develop solutions that provide both Application-Specific Integrated Circuit-like EE and Field-Programmable Gate Array-like flexibility. One such solution is Coarse Grain Reconfigurable Array (CGRA). Over the past decades, CGRAs have evolved and are competing to become mainstream hardware accelerators, especially for accelerating Digital Signal Processing (DSP) applications. Due to the over-specialization of computing architectures, the focus is shifting towards fitting an extensive data representation range into fewer bits, e.g., a 32-bit space can represent a more extensive data range with floating-point (FP) representation than an integer representation. Computation using FP representation requires numerous encodings and leads to complex circuits for the FP operators, decreasing the EE of the entire system. This thesis presents the design of an EE ultra-low-power CGRA with native support for FP computation by leveraging an emerging paradigm of approximate computing called transprecision computing. We also present the contributions in the compilation toolchain and system-level integration of CGRA in a System-on-Chip, to envision the proposed CGRA as an EE hardware accelerator. Finally, an extensive set of experiments using real-world algorithms employed in near-sensor processing applications are performed, and results are compared with state-of-the-art (SoA) architectures. It is empirically shown that our proposed CGRA provides better results w.r.t. SoA architectures in terms of power, performance, and area

Ultra-Low Power IoT Smart Visual Sensing Devices for Always-ON Applications

This work presents the design of a Smart Ultra-Low Power visual sensor architecture that couples together an ultra-low power event-based image sensor with a parallel and power-optimized digital architecture for data processing. By means of mixed-signal circuits, the imager generates a stream of address events after the extraction and binarization of spatial gradients. When targeting monitoring applications, the sensing and processing energy costs can be reduced by two orders of magnitude thanks to either the mixed-signal imaging technology, the event-based data compression and the use of event-driven computing approaches. From a system-level point of view, a context-aware power management scheme is enabled by means of a power-optimized sensor peripheral block, that requests the processor activation only when a relevant information is detected within the focal plane of the imager. When targeting a smart visual node for triggering purpose, the event-driven approach brings a 10x power reduction with respect to other presented visual systems, while leading to comparable results in terms of detection accuracy. To further enhance the recognition capabilities of the smart camera system, this work introduces the concept of event-based binarized neural networks. By coupling together the theory of binarized neural networks and focal-plane processing, a 17.8% energy reduction is demonstrated on a real-world data classification with a performance drop of 3% with respect to a baseline system featuring commercial visual sensors and a Binary Neural Network engine. Moreover, if coupling the BNN engine with the event-driven triggering detection flow, the average power consumption can be as low as the sleep power of 0.3mW in case of infrequent events, which is 8x lower than a smart camera system featuring a commercial RGB imager

Towards an embedded board-level tester: study of a configurable test processor

The demand for electronic systems with more features, higher performance, and less power consumption increases continuously. This is a real challenge for design and test engineers because they have to deal with electronic systems with ever-increasing complexity maintaining production and test costs low and meeting critical time to market deadlines. For a test engineer working at the board-level, this means that manufacturing defects must be detected as soon as possible and at a low cost. However, the use of classical test techniques for testing modern printed circuit boards is not sufficient, and in the worst case these techniques cannot be used at all. This is mainly due to modern packaging technologies, a high device density, and high operation frequencies of modern printed circuit boards. This leads to very long test times, low fault coverage, and high test costs. This dissertation addresses these issues and proposes an FPGA-based test approach for printed circuit boards. The concept is based on a configurable test processor that is temporarily implemented in the on-board FPGA and provides the corresponding mechanisms to communicate to external test equipment and co-processors implemented in the FPGA. This embedded test approach provides the flexibility to implement test functions either in the external test equipment or in the FPGA. In this manner, tests are executed at-speed increasing the fault coverage, test times are reduced, and the test system can be adapted automatically to the properties of the FPGA and devices located on the board. An essential part of the FPGA-based test approach deals with the development of a test processor. In this dissertation the required properties of the processor are discussed, and it is shown that the adaptation to the specific test scenario plays a very important role for the optimization. For this purpose, the test processor is equipped with configuration parameters at the instruction set architecture and microarchitecture level. Additionally, an automatic generation process for the test system and for the computation of some of the processor’s configuration parameters is proposed. The automatic generation process uses as input a model known as the device under test model (DUT-M). In order to evaluate the entire FPGA-based test approach and the viability of a processor for testing printed circuit boards, the developed test system is used to test interconnections to two different devices: a static random memory (SRAM) and a liquid crystal display (LCD). Experiments were conducted in order to determine the resource utilization of the processor and FPGA-based test system and to measure test time when different test functions are implemented in the external test equipment or the FPGA. It has been shown that the introduced approach is suitable to test printed circuit boards and that the test processor represents a realistic alternative for testing at board-level.Der Bedarf an elektronischen Systemen mit zusätzlichen Merkmalen, höherer Leistung und geringerem Energieverbrauch nimmt ständig zu. Dies stellt eine erhebliche Herausforderung für Entwicklungs- und Testingenieure dar, weil sie sich mit elektronischen Systemen mit einer steigenden Komplexität zu befassen haben. Außerdem müssen die Herstellungs- und Testkosten gering bleiben und die Produkteinführungsfristen so kurz wie möglich gehalten werden. Daraus folgt, dass ein Testingenieur, der auf Leiterplatten-Ebene arbeitet, die Herstellungsfehler so früh wie möglich entdecken und dabei möglichst niedrige Kosten verursachen soll. Allerdings sind die klassischen Testmethoden nicht in der Lage, die Anforderungen von modernen Leiterplatten zu erfüllen und im schlimmsten Fall können diese Testmethoden überhaupt nicht verwendet werden. Dies liegt vor allem an modernen Gehäuse-Technologien, der hohen Bauteildichte und den hohen Arbeitsfrequenzen von modernen Leiterplatten. Das führt zu sehr langen Testzeiten, geringer Testabdeckung und hohen Testkosten. Die Dissertation greift diese Problematik auf und liefert einen FPGA-basierten Testansatz für Leiterplatten. Das Konzept beruht auf einem konfigurierbaren Testprozessor, welcher im On-Board-FPGA temporär implementiert wird und die entsprechenden Mechanismen für die Kommunikation mit der externen Testeinrichtung und Co-Prozessoren im FPGA bereitstellt. Dadurch ist es möglich Testfunktionen flexibel entweder auf der externen Testeinrichtung oder auf dem FPGA zu implementieren. Auf diese Weise werden Tests at-speed ausgeführt, um die Testabdeckung zu erhöhen. Außerdem wird die Testzeit verkürzt und das Testsystem automatisch an die Eigenschaften des FPGAs und anderer Bauteile auf der Leiterplatte angepasst. Ein wesentlicher Teil des FPGA-basierten Testansatzes umfasst die Entwicklung eines Testprozessors. In dieser Dissertation wird über die benötigten Eigenschaften des Prozessors diskutiert und es wird gezeigt, dass die Anpassung des Prozessors an den spezifischen Testfall von großer Bedeutung für die Optimierung ist. Zu diesem Zweck wird der Prozessor mit Konfigurationsparametern auf der Befehlssatzarchitektur-Ebene und Mikroarchitektur-Ebene ausgerüstet. Außerdem wird ein automatischer Generierungsprozess für die Realisierung des Testsystems und für die Berechnung einer Untergruppe von Konfigurationsparametern des Prozessors vorgestellt. Der automatische Generierungsprozess benutzt als Eingangsinformation ein Modell des Prüflings (device under test model, DUT-M). Das entwickelte Testsystem wurde zum Testen von Leiterplatten für Verbindungen zwischen dem FPGA und zwei Bauteilen verwendet, um den FPGA-basierten Testansatz und die Durchführbarkeit des Testprozessors für das Testen auf Leiterplatte-Ebene zu evaluieren. Die zwei Bauteile sind ein Speicher mit direktem Zugriff (static random-access memory, SRAM) und eine Flüssigkristallanzeige (liquid crystal display, LCD). Die Experimente wurden durchgeführt, um den Ressourcenverbrauch des Prozessors und Testsystems festzustellen und um die Testzeit zu messen. Dies geschah durch die Implementierung von unterschiedlichen Testfunktionen auf der externen Testeinrichtung und dem FPGA. Dadurch konnte gezeigt werden, dass der FPGA-basierte Ansatz für das Testen von Leiterplatten geeignet ist und dass der Testprozessor eine realistische Alternative für das Testen auf Leiterplatten-Ebene ist

Tailoring instruction-set extensions for an ultra-low power tightly-coupled cluster of OpenRISC cores

none8siNome progetto: NanoTeraBaseline RISC instruction sets for ultra-low power processors are constantly being tuned to reduce cycle count when executing computation-intensive applications. Performance improvements often come at a non-negligible price in terms of area and critical path length and imply deeper pipelines and complex memory interfaces. This penalizes control-intensive code execution and significantly increases cost and complexity of building multi-core clusters. In addition, some extensions are not easily exploited by compilers and may increase code development effort, especially when considering parallel applications. In this paper we describe our efforts in enhancing a baseline open ISA (OpenRISC) and its LLVM compiler back-end to significantly reduce execution cycles while minimizing the impact on core micro-architecture complexity, number of pipeline stages, area and power. In addition, we improved the core micro-architecture to streamline its integration in a tightly-coupled cluster, sharing instruction cache and data memory, thereby further enhancing parallel execution efficiency. The combined effect of ISA, compiler and micro-architecture evolution gives an average energy efficiency boost of 59% on vector intensive code and 41% otherwise, at an area and power increase of 2.3% and 18% on a four-core processor cluster.openGautschi, Michael; Traber, Andreas; Pullini, Antonio; Benini, Luca; Scandale, Michele; Di Federico, Alessandro; Beretta, Michele; Agosta, GiovanniGautschi, Michael; Traber, Andreas; Pullini, Antonio; Benini, Luca; Scandale, Michele; Di Federico, Alessandro; Beretta, Michele; Agosta, Giovann

Proceedings of the 5th International Workshop on Reconfigurable Communication-centric Systems on Chip 2010 - ReCoSoC\u2710 - May 17-19, 2010 Karlsruhe, Germany. (KIT Scientific Reports ; 7551)

ReCoSoC is intended to be a periodic annual meeting to expose and discuss gathered expertise as well as state of the art research around SoC related topics through plenary invited papers and posters. The workshop aims to provide a prospective view of tomorrow\u27s challenges in the multibillion transistor era, taking into account the emerging techniques and architectures exploring the synergy between flexible on-chip communication and system reconfigurability