Power Management of Server Farms

Iazeolla, Giuseppe; Pieroni, Alessandra

doi:10.4028/www.scientific.net/amm.492.453

Cited by 8 publications

(5 citation statements)

References 4 publications

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“…In Fig.7 the CNN power consumption is provided. Power consumption nowadays represents a crucial aspect especially for embedded systems where the power supply is usually provided by batteries [30][31][32][33][34][35][36]. There are three power dissipation components in CMOS digital circuits and consequently in microprocessors [23]: Among these contributions, the switching power represents the main one, and it is defined in Eq.2, where a is the switching activity, C is the switching capacitance, f is the clock frequency and Vdd the supply voltage.…”

Section: Resultsmentioning

confidence: 99%

FPGA Implementation of Hand-written Number Recognition Based on CNN

Giardino

Matta

Silvestri

et al. 2019

Int. J. Adv. Sci. Eng. Inf. Technol.

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Convolutional Neural Networks (CNNs) are the state-of-the-art in computer vision for different purposes such as image and video classification, recommender systems and natural language processing. The connectivity pattern between CNNs neurons is inspired by the structure of the animal visual cortex. In order to allow the processing, they are realized with multiple parallel 2dimensional FIR filters that convolve the input signal with the learned feature maps. For this reason, a CNN implementation requires highly parallel computations that cannot be achieved using traditional general-purpose processors, which is why they benefit from a very significant speed-up when mapped and run on Field Programmable Gate Arrays (FPGAs). This is because FPGAs offer the capability to design full customizable hardware architectures, providing high flexibility and the availability of hundreds to thousands of on-chip Digital Signal Processing (DSP) blocks. This paper presents an FPGA implementation of a handwritten number recognition system based on CNN. The system has been characterized in terms of classification accuracy, area, speed, and power consumption. The neural network was implemented on a Xilinx XC7A100T FPGA, and it uses 29.69% of Slice LUTs, 4.42% of slice registers and 52.50% block RAMs. We designed the system using a 9-bit representation that allows for avoiding the use of DSP. For this reason, multipliers are implemented using LUTs. The proposed architecture can be easily scaled on different FPGA devices thank its regularity. CNN can reach a classification accuracy of 90%.

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Section: Resultsmentioning

confidence: 99%

FPGA Implementation of Hand-written Number Recognition Based on CNN

Giardino

Matta

Silvestri

et al. 2019

Int. J. Adv. Sci. Eng. Inf. Technol.

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“…Notice that the power has been estimated using a worst-case approach considering an activity factor of 0.5 on every node of the synthesized network. The implementation results of AW-SOM architectures with 1, 2, 3 and 4 features are shown respectively in Tables II, III, IV, and V. The AW-SOM IPs have also been characterized in terms of power consumption that nowadays represents a crucial aspect both for embed systems and desktop [36]. To better understand the implementation results and the quality of the generated architecture, we show the implementation results in an aggregated form in Figs.…”

Section: Implementation Resultsmentioning

confidence: 99%

An Automatic AW-SOM VHDL IP-core Generator

Giardino

Matta

Spanò

2019

Int. J. Adv. Sci. Eng. Inf. Technol.

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In this paper, the authors present a MATLAB IP generator for hardware accelerators of All-Winner Self-Organizing Maps (AW-SOM). AW-SOM is a modified version of Kohonen's Self Organizing Maps (SOM) algorithm, which is one of the most used Machine Learning algorithms for data clustering, and vector quantization. The architecture of the AW-SOM method is meant for hardware implementations, and its main feature is a processing speed almost independent to the number of neurons since each of them is processed in a parallel way; the parallelization can be easily exploited by hardware custom hardware designs. The IP generator is built-in MATLAB and provides the user with the possibility to design a custom and efficient hardware accelerator. Several settings can be set such as the number of features and the number of neurons. The target language is the VHSIC Hardware Description Language (VHDL). The generated IP cores can be used for the training of the model and a built-in function of the software can also check the clustering performances using its inference capabilities. The accelerators produced by the software have been also characterized in terms of max frequency, hardware resources, and power consumption. The authors performed the hardware implementations on a XILINX Virtex 7 xc7vx690t FPGA.

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“…• Health parameters are usually low-speed signals and consequently, the processing speed of microcontrollers is sufficient. As previously discussed the power consumption represents one of the most important aspects of Health wearable systems [69], [70]. For this reason, it could be interesting investigate solutions to further reduce the power consumption microcontrollers.…”

Section: G Processing Elementmentioning

confidence: 99%

E-health-IoT Universe: A Review

Scarpato¹,

Pieroni²,

Nunzio³

et al. 2017

International Journal on Advanced Science, Engineering and Information Technology

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The Internet of Things (IoT) devices are able to collect and share data directly with other devices through the cloud environment, providing a huge amount of information to be gathered, stored and analyzed for data-analytics processes. The scenarios in which the IoT devices may be useful are amazing varying, from automotive, to industrial automation or remote monitoring of domestic environment. Furthermore, has been proved that healthcare applications represent an important field of interest for IoT devices, due to the capability of improving the access to care, reducing the cost of healthcare and most importantly increasing the quality of life of the patients. In this paper, we analyze the state-of-art of IoT in medical environment, illustrating an extended range of IoT-driven healthcare applications that, however, still need innovative and high technology-based solutions to be considered ready to market. In particular, problems regarding characteristics of response-time and precision will be examined. Furthermore, wearable and energy saving properties will be investigated in this paper and also the IT architectures able to ensure security and privacy during the all data-transmission process. Finally, considerations about data mining applications, such as risks prediction, classification and clustering will be provided, that are considered fundamental issues to ensure the accuracy of the care processes.

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Power Management of Server Farms

Cited by 8 publications

References 4 publications

FPGA Implementation of Hand-written Number Recognition Based on CNN

FPGA Implementation of Hand-written Number Recognition Based on CNN

An Automatic AW-SOM VHDL IP-core Generator

E-health-IoT Universe: A Review

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