Power estimation on functional level for programmable processors

In diesem Beitrag werden verschiedene Ansätze zur Verlustleistungsschätzung von programmierbaren Prozessoren vorgestellt und bezüglich ihrer Übertragbarkeit auf moderne Prozessor-Architekturen wie beispielsweise Very Long Instruction Word (VLIW)-Architekturen bewertet. Besonderes Augenmerk liegt hierbei auf dem Konzept der sogenannten Functional-Level Power Analysis (FLPA). Dieser Ansatz basiert auf der Einteilung der Prozessor-Architektur in funktionale Blöcke wie beispielsweise Processing-Unit, Clock-Netzwerk, interner Speicher und andere. Die Verlustleistungsaufnahme dieser Bl¨ocke wird parameterabhängig durch arithmetische Modellfunktionen beschrieben. Durch automatisierte Analyse von Assemblercodes des zu schätzenden Systems mittels eines Parsers können die Eingangsparameter wie beispielsweise der erzielte Parallelitätsgrad oder die Art des Speicherzugriffs gewonnen werden. Dieser Ansatz wird am Beispiel zweier moderner digitaler Signalprozessoren durch eine Vielzahl von Basis-Algorithmen der digitalen Signalverarbeitung evaluiert. Die ermittelten Schätzwerte für die einzelnen Algorithmen werden dabei mit physikalisch gemessenen Werten verglichen. Es ergibt sich ein sehr kleiner maximaler Schätzfehler von 3%.</p><p style=&quot;line-height: 20px;&quot;> In this contribution different approaches for power estimation for programmable processors are presented and evaluated concerning their capability to be applied to modern digital signal processor architectures like e.g. Very Long InstructionWord (VLIW) -architectures. Special emphasis will be laid on the concept of so-called Functional-Level Power Analysis (FLPA). This approach is based on the separation of the processor architecture into functional blocks like e.g. processing unit, clock network, internal memory and others. The power consumption of these blocks is described by parameter dependent arithmetic model functions. By application of a parser based automized analysis of assembler codes of the systems to be estimated the input parameters of the Correspondence to: H. Blume ([email protected]) arithmetic functions like e.g. the achieved degree of parallelism or the kind and number of memory accesses can be computed. This approach is exemplarily demonstrated and evaluated applying two modern digital signal processors and a variety of basic algorithms of digital signal processing. The resulting estimation values for the inspected algorithms are compared to physically measured values. A resulting maximum estimation error of 3% is achieved

Digital signal processor fundamentals and system design

Digital Signal Processors (DSPs) have been used in accelerator systems for more than fifteen years and have largely contributed to the evolution towards digital technology of many accelerator systems, such as machine protection, diagnostics and control of beams, power supply and motors. This paper aims at familiarising the reader with DSP fundamentals, namely DSP characteristics and processing development. Several DSP examples are given, in particular on Texas Instruments DSPs, as they are used in the DSP laboratory companion of the lectures this paper is based upon. The typical system design flow is described; common difficulties, problems and choices faced by DSP developers are outlined; and hints are given on the best solution

Domain-Specific Computing Architectures and Paradigms

We live in an exciting era where artificial intelligence (AI) is fundamentally shifting the dynamics of industries and businesses around the world. AI algorithms such as deep learning (DL) have drastically advanced the state-of-the-art cognition and learning capabilities. However, the power of modern AI algorithms can only be enabled if the underlying domain-specific computing hardware can deliver orders of magnitude more performance and energy efficiency. This work focuses on this goal and explores three parts of the domain-specific computing acceleration problem; encapsulating specialized hardware and software architectures and paradigms that support the ever-growing processing demand of modern AI applications from the edge to the cloud. This first part of this work investigates the optimizations of a sparse spatio-temporal (ST) cognitive system-on-a-chip (SoC). This design extracts ST features from videos and leverages sparse inference and kernel compression to efficiently perform action classification and motion tracking. The second part of this work explores the significance of dataflows and reduction mechanisms for sparse deep neural network (DNN) acceleration. This design features a dynamic, look-ahead index matching unit in hardware to efficiently discover fine-grained parallelism, achieving high energy efficiency and low control complexity for a wide variety of DNN layers. Lastly, this work expands the scope to real-time machine learning (RTML) acceleration. A new high-level architecture modeling framework is proposed. Specifically, this framework consists of a set of high-performance RTML-specific architecture design templates, and a Python-based high-level modeling and compiler tool chain for efficient cross-stack architecture design and exploration.PHDElectrical and Computer EngineeringUniversity of Michigan, Horace H. Rackham School of Graduate Studieshttp://deepblue.lib.umich.edu/bitstream/2027.42/162870/1/lchingen_1.pd

Design and Implementation of Software Defined Radios on a Homogeneous Multi-Processor Architecture

In the wireless communications domain, multi-mode and multi-standard platforms are becoming increasingly the central focus of system architects. In fact, mobile terminal users require more and more mobility and throughput, pushing towards a fully integrated radio system able to support different communication protocols running concurrently on the platform. A new concept of radio system was introduced to meet the users' expectations. Flexible radio platforms have became an indispensable requirement to meet the expectations of the users today and in the future. This thesis deals with issues related to the design of flexible radio platforms. In particular, the flexibility of the radio system is achieved through the concept of software defined radios (SDRs). The research work focuses on the utilization of homogeneous multi-processor (MP) architectures as a feasible way to efficiently implement SDR platforms. In fact, platforms based on MP architectures are able to deliver high performance together with a high degree of flexibility. Moreover, homogeneous MP platforms are able to reduce design and verification costs as well as provide a high scalability in terms of software and hardware. However, homogeneous MP architectures provide less computational efficiency when compared to heterogeneous solutions. This thesis can be divided into two parts: the first part is related to the implementation of a reference platform while the second part of the thesis introduces the design and implementation of flexible, high performance, power and energy efficient algorithms for wireless communications. The proposed reference platform, Ninesilica, is a homogeneous MP architecture composed of a 3x3 mesh of processing nodes (PNs), interconnected by a hierarchical Network-on-Chip (NoC). Each PN hosts as Processing Element (PE) a processor core. To improve the computational efficiency of the platform, different power and energy saving techniques have been investigated. In the design, implementation and mapping of the algorithms, the following constraints were considered: energy and power efficiency, high scalability of the platform, portability of the solutions across similar platforms, and parallelization efficiency. Ninesilica architecture together with the proposed algorithm implementations showed that homogeneous MP architectures are highly scalable platforms, both in terms of hardware and software. Furthermore, Ninesilica architecture demonstrated that homogeneous MPs are able to achieve high parallelization efficiency as well as high energy and power savings, meeting the requirements of SDRs as well as enabling cognitive radios. Ninesilica can be utilized as a stand-alone block or as an elementary building block to realize clustered many-core architectures. Moreover, the obtained results, in terms of parallelization efficiency as well as power and energy efficiency are independent of the type of PE utilized, ensuring the portability of the results to similar architectures based on a different type of processing element