1,288 research outputs found

    Low Power Processor Architectures and Contemporary Techniques for Power Optimization – A Review

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    The technological evolution has increased the number of transistors for a given die area significantly and increased the switching speed from few MHz to GHz range. Such inversely proportional decline in size and boost in performance consequently demands shrinking of supply voltage and effective power dissipation in chips with millions of transistors. This has triggered substantial amount of research in power reduction techniques into almost every aspect of the chip and particularly the processor cores contained in the chip. This paper presents an overview of techniques for achieving the power efficiency mainly at the processor core level but also visits related domains such as buses and memories. There are various processor parameters and features such as supply voltage, clock frequency, cache and pipelining which can be optimized to reduce the power consumption of the processor. This paper discusses various ways in which these parameters can be optimized. Also, emerging power efficient processor architectures are overviewed and research activities are discussed which should help reader identify how these factors in a processor contribute to power consumption. Some of these concepts have been already established whereas others are still active research areas. © 2009 ACADEMY PUBLISHER

    Coarse-grained reconfigurable array architectures

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    Coarse-Grained ReconïŹgurable Array (CGRA) architectures accelerate the same inner loops that beneïŹt from the high ILP support in VLIW architectures. By executing non-loop code on other cores, however, CGRAs can focus on such loops to execute them more efïŹciently. This chapter discusses the basic principles of CGRAs, and the wide range of design options available to a CGRA designer, covering a large number of existing CGRA designs. The impact of different options on ïŹ‚exibility, performance, and power-efïŹciency is discussed, as well as the need for compiler support. The ADRES CGRA design template is studied in more detail as a use case to illustrate the need for design space exploration, for compiler support and for the manual ïŹne-tuning of source code

    Template Generation - A Graph Profiling Algorithm

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    The availability of high-level design entry tooling is crucial for the viability of any reconfigurable SoC architecture. This paper presents a template generation algorithm. The objective of template generation step is to extract functional equivalent structures, i.e. templates, from a control data flow graph. By profiling the graph, the algorithm generates all the possible templates and the corresponding matches. Using unique serial numbers and circle numbers, the algorithm can find all distinct templates with multiple outputs. A new type of graph (hydragraph) that can cope with multiple outputs is introduced. The generated templates pepresented by the hydragraph are not limited in shapes, i.e., we can find templates with multiple outputs or multiple sinks

    BrainTTA: A 35 fJ/op Compiler Programmable Mixed-Precision Transport-Triggered NN SoC

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    Recently, accelerators for extremely quantized deep neural network (DNN) inference with operand widths as low as 1-bit have gained popularity due to their ability to largely cut down energy cost per inference. In this paper, a flexible SoC with mixed-precision support is presented. Contrary to the current trend of fixed-datapath accelerators, this architecture makes use of a flexible datapath based on a Transport-Triggered Architecture (TTA). The architecture is fully programmable using C. The accelerator has a peak energy efficiency of 35/67/405 fJ/op (binary, ternary, and 8-bit precision) and a throughput of 614/307/77 GOPS, which is unprecedented for a programmable architecture

    Low power architectures for streaming applications

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    BrainTTA: A 35 fJ/op Compiler Programmable Mixed-Precision Transport-Triggered NN SoC

    Get PDF
    Recently, accelerators for extremely quantized deep neural network (DNN) inference with operand widths as low as 1-bit have gained popularity due to their ability to largely cut down energy cost per inference. In this paper, a flexible SoC with mixed-precision support is presented. Contrary to the current trend of fixed-datapath accelerators, this architecture makes use of a flexible datapath based on a Transport-Triggered Architecture (TTA). The architecture is fully programmable using C. The accelerator has a peak energy efficiency of 35/67/405 fJ/op (binary, ternary, and 8-bit precision) and a throughput of 614/307/77 GOPS, which is unprecedented for a programmable architecture
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