Modified Frequency Scaling Algorithm for FMCW SAR Data Processing

This paper presents a modified frequency scaling algorithm for frequency modulated continuous wave synthetic aperture radar （FMCW SAR） data processing. The relative motion between radar and target in FMCW SAR during reception and between transmission and reception will introduce serious dilation in the received signal. The dilation can cause serious distortions in the reconstructed images using conventional signal processing methods. The received signal is derived and the received signal in range-Doppler domain is given. The relation between the phase resulting from antenna motion and the azimuth frequency is analyzed. The modified frequency scaling algorithm is proposed to process the received signal with serious dilation. The algorithm can effectively eliminate the impact of the dilation. The algorithm performances are shown by the simulation results.

Energy Scaling of Terahertz Pulses Produced through Difference Frequency Generation

We study the energy scaling of terahertz (THz) emission through difference frequency generation of near-infrared pulses, and demonstrate that Gigawatt few-cycle THz transients at the central frequency of 30 THz are produced from GaSe crystal pumped by two pulses at 1.65 and 1.95 micrometers, with the high quantum yield of 28%. Our analysis indicates that the high yield of DFG originates from the largely reduced group velocity mismatch as the long-wavelength pumping pulses are employed.

Energy-efficient reconfigurable processor for QC-LDPC via adaptive coding-voltage-frequency tuning

To apply a quasi-cyclic low density parity check(QC-LDPC)to different scenarios,a data-stream driven pipelined macro instruction set and a reconfigurable processor architecture are proposed for the typical QC-LDPC algorithm.The data-level parallelism is improved by instructions to dynamically configure the multi-core computing units.Simultaneously,an intelligent adjustment strategy based on a programmable wake-up controller(WuC)is designed so that the computing mode,operating voltage,and frequency of the QC-LDPC algorithm can be adjusted.This adjustment can improve the computing efficiency of the processor.The QC-LDPC processors are verified on the Xilinx ZCU102 field programmable gate array(FPGA)board and the computing efficiency is measured.The experimental results indicate that the QC-LDPC processor can support two encoding lengths of three typical QC-LDPC algorithms and 20 adaptive operating modes of operating voltage and frequency.The maximum efficiency can reach up to 12.18 Gbit/(s·W),which is more flexible than existing state-of-the-art processors for QC-LDPC.

A Hybrid Model for Reliability Aware and Energy-Efficiency in Multicore Systems

Recently,Multicore systems use Dynamic Voltage/Frequency Scaling(DV/FS)technology to allow the cores to operate with various voltage and/or frequencies than other cores to save power and enhance the performance.In this paper,an effective and reliable hybridmodel to reduce the energy and makespan in multicore systems is proposed.The proposed hybrid model enhances and integrates the greedy approach with dynamic programming to achieve optimal Voltage/Frequency(Vmin/F)levels.Then,the allocation process is applied based on the availableworkloads.The hybrid model consists of three stages.The first stage gets the optimum safe voltage while the second stage sets the level of energy efficiency,and finally,the third is the allocation stage.Experimental results on various benchmarks show that the proposed model can generate optimal solutions to save energy while minimizing the makespan penalty.Comparisons with other competitive algorithms show that the proposed model provides on average 48%improvements in energy-saving and achieves an 18%reduction in computation time while ensuring a high degree of system reliability.

Runtime Energy Savings Based on Machine Learning Models for Multicore Applications

To improve the power consumption of parallel applications at the runtime, modern processors provide frequency scaling and power limiting capabilities. In this work, a runtime strategy is proposed to maximize energy savings under a given performance degradation. Machine learning techniques were utilized to develop performance models which would provide accurate performance prediction with change in operating core-uncore frequency. Experiments, performed on a node (28 cores) of a modern computing platform showed significant energy savings of as much as 26% with performance degradation of as low as 5% under the proposed strategy compared with the execution in the unlimited power case.

FSM Based DFS Link for Network on Chip

As low power consumption is the main design issue involved in a network on chip (NoC), researchers are concentrating more on both algorithms and architectural approaches. The conventional Dynamic Frequency Scaling (DFS) and history based Frequency Scaling (HDFS) algorithms are utilized to process the energy constrained data traffic. However, these conventional algorithms achieve higher energy efficiencies, and they result in performance degradation due to the auxiliary latency between clock domains. In this paper, we present a variable power optimization interface for NoC using a Finite State Machine (FSM) approach to attain better performance improvement. The parameters are estimated using 45 nm TSMCCMOS technology. In comparison with DFS system, the evaluation results show that FSM-DFS link achieves 81.55% dynamic power savings on the links in the on-chip network, and 37.5% leakage power savings of the link. Also, this proposed work is evaluated for various performance parameters and compared with conventional work. The simulation results are superior to conventional work.

More Bang for Your Buck:Boosting Performance with Capped Power Consumption

Achieving faster performance without increasing power and energy consumption for computing systems is an outstanding challenge.This paper develops a novel resource allocation scheme for memory-bound applications running on High-Performance Computing(HPC)clusters,aiming to improve application performance without breaching peak power constraints and total energy consumption.Our scheme estimates how the number of processor cores and CPU frequency setting affects the application performance.It then uses the estimate to provide additional compute nodes to memory-bound applications if it is profitable to do so.We implement and apply our algorithm to 12 representative benchmarks from the NAS parallel benchmark and HPC Challenge(HPCC)benchmark suites and evaluate it on a representative HPC cluster.Experimental results show that our approach can effectively mitigate memory contention to improve application performance,and it achieves this without significantly increasing the peak power and overall energy consumption.Our approach obtains on average 12.69%performance improvement over the default resource allocation strategy,but uses 7.06%less total power,which translates into 17.77%energy savings.

Energy Efficient Block-Partitioned Multicore Processors for Parallel Applications

Due to the increasing power consumption in modern computing systems, energy management has become an important research area in the last decade. Recently, multicore has emerged to be an energy efficient architecture that exploits parallelisms in modern applications. However, as the number of cores on a single chip continues to increase, it has been a grand challenge on how to effectively manage the energy efficiency of multicore-based systems. In this paper, based on the voltage island and dynamic voltage and frequency scaling （DVFS） techniques, we investigate the energy efficiency of block-partitioned multieore processors, where cores are grouped into blocks with the cores on one block sharing a DVFS- enabled power supply. Depending on the number of cores on each block, we study both symmetric and asymmetric block configurations. We develop a system-level power model （which can support various power management techniques） and derive both block- and system-wide energy-efficient frequencies for systems with block-partitioned multieore processors. Based on the power model, we prove that, for embarrassingly parallel applications, having all cores on a single block can achieve the same energy savings as that of the individual block configuration （where each core forms a single block and has its own power supply）. However, for applications with limited degrees of parallelism, we show the superiority of the buddy-asymmetric block configuration, where the number of required blocks （and power supplies） is logarithmically related to the number of cores on the chip, in that it can achieve the same amount of energy savings as that of the individual block configuration. The energy efficiency of different block configurations is further evaluated through extensive simulations with both synthetic as well as a real life application.

Probabilistic Delay Fault Model for DVFS Circuits

Decreasing the power supply voltage in dynamic voltage frequency scaling to save power con- sumption may introduce extra delays in CMOS circuits, which may cause errors. This paper presents the probabilistic delay fault model （PDFM）, which describes the probability of an error occurring as a function of the power supply voltage and the clock period in synchronous CMOS circuits. In a wide range of applica- tions （graphic, video, digital filtering, etc.）, errors occurring with low probability and not remaining for a long time are acceptable. For combinational circuits which have long critical paths with low probability of excita- tion, a performance increase is achieved with a certain rate of errors determined by the PDFM compared with the traditional design which considers the worst case. The PDFM applied to array multipliers and ripple carry adders shows the agreement of the predicted probabilities with simulated delay histograms to support the practicality of using the PDFM to select power supply voltage and clock period in dynamic voltage fre- quency scaling circuits with tolerable error rates.