Fast and Accurate Estimation of Quality of Results in High-Level Synthesis with Machine Learning

2018 ◽  
Author(s):  
Steve Dai ◽  
Yuan Zhou ◽  
Hang Zhang ◽  
Ecenur Ustun ◽  
Evangeline F.Y. Young ◽  
...  
Keyword(s):  
Machine Learning ◽  
High Level Synthesis ◽  
Accurate Estimation ◽  
Quality Of Results ◽  
High Level

scholarly journals An FPGA-Based LOCO-ANS Implementation for Lossless and Near-Lossless Image Compression Using High-Level Synthesis

Electronics ◽  
2021 ◽  
Vol 10 (23) ◽  
pp. 2934
Author(s):  
Tobías Alonso ◽  
Gustavo Sutter ◽  
Jorge E. López de Vergara
Keyword(s):  
Hardware Implementation ◽  
Low Complexity ◽  
High Level Synthesis ◽  
Raspberry Pi ◽  
Quality Of Results ◽  
Design Time ◽  
Numeral Systems ◽  
High Level ◽  
Image Compressor

In this work, we present and evaluate a hardware architecture for the LOCO-ANS (Low Complexity Lossless Compression with Asymmetric Numeral Systems) lossless and near-lossless image compressor, which is based on JPEG-LS standard. The design is implemented in two FPGA generations, evaluating its performance for different codec configurations. The tests show that the design is capable of up to 40.5 MPixels/s and 124 MPixels/s per lane for Zynq 7020 and UltraScale+ FPGAs, respectively. Compared to the single thread LOCO-ANS software implementation running in a 1.2 GHz Raspberry Pi 3B, each hardware lane achieves 6.5 times higher throughput, even when implemented in an older and cost-optimized chip like the Zynq 7020. Results are also presented for a lossless only version, which achieves a lower footprint and approximately 50% higher performance than the version that supports both lossless and near-lossless. Interestingly, these great results were obtained applying High-Level Synthesis, describing the coder with C++ code, which tends to establish a trade-off between design time and quality of results. These results show that the algorithm is very suitable for hardware implementation. Moreover, the implemented system is faster and achieves higher compression than the best previously available near-lossless JPEG-LS hardware implementation.

Quality of Experience Models for Multimedia Streaming

2010 ◽  
Vol 2 (4) ◽  
pp. 1-20 ◽  
Author(s):  
Vlado Menkovski ◽  
Georgios Exarchakos ◽  
Antonio Liotta ◽  
Antonio Cuadra Sánchez
Keyword(s):  
Machine Learning ◽  
Quality Of Experience ◽  
Prediction Models ◽  
Multimedia Streaming ◽  
Accurate Estimation ◽  
Management Service ◽  
Subjective Data ◽  
Efficient Management ◽  
Learning Techniques

Understanding how quality is perceived by viewers of multimedia streaming services is essential for efficient management of those services. Quality of Experience (QoE) is a subjective metric that quantifies the perceived quality, which is crucial in the process of optimizing tradeoff between quality and resources. However, accurate estimation of QoE often entails cumbersome studies that are long and expensive to execute. In this regard, the authors present a QoE estimation methodology for developing Machine Learning prediction models based on initial restricted-size subjective tests. Experimental results on subjective data from streaming multimedia tests show that the Machine Learning models outperform other statistical methods achieving accuracy greater than 90%. These models are suitable for real-time use due to their small computational complexity. Even though they have high accuracy, these models are static and cannot adapt to environmental change. To maintain the accuracy of the prediction models, the authors have adopted Online Learning techniques that update the models on data from subjective viewer feedback. This method provides accurate and adaptive QoE prediction models that are an indispensible component of a QoE-aware management service.

scholarly journals Power Efficient Rapid Design Space Exploration of Integrated Scheduling and Module Selection in High Level Synthesis

2021 ◽  
Author(s):  
Pallabi Sarkar
Keyword(s):  
Power Consumption ◽  
Design Space Exploration ◽  
Design Space ◽  
Space Exploration ◽  
High Level Synthesis ◽  
Optimal Solutions ◽  
Power Efficient ◽  
Pi Method ◽  
High Level

High level Synthesis (HLS) or Electronic System Level (ESL) synthesis requires scheduling algorithms that have strong capability to reach optimal/near-optimal solutions with significant rapidity and greater accuracy. A novel power efficient scheduling approach using ‘PI’ method has been presented in this thesis that reduces the final power consumption of the solution at the expenditure of minimal latency clock cycles. The proposed scheduling approach is based on ‘Priority indicator (PI)’ metric and ‘Intersect Matrix’ topology methods that have a tendency to escape local optimal solutions and thereby reach global solutions. Application of the proposed approach results in even distribution of allocated hardware functional units thereby yielding power efficient scheduling solutions. The two main novel and significant aspects of the thesis are: a) Introduction of ‘Intersect Matrix’ topology with its associated algorithm which is used to check for precedence violation during scheduling b) Introduction of PI method using Priority indicator metric that assists in choosing the highest priority node during each iteration of the scheduling optimization process. Comparative analysis of the proposed approach has been done with an existing design space exploration method for qualitative assessment using proposed ‘Quality Cost Factor (Q- metric)’. This Q-metric is a combination of latency and power consumption values for the solution found, which dictates the quality of the final solutions found in terms of cost for both the proposed and existing approaches. An average improvement of approximately 12 % in quality of final solution and average reduction of 59 % in runtime has been achieved by the proposed approach compared to a current scheduling approach for the DSP benchmarks.

scholarly journals Botnet Forensic Analysis Using Machine Learning

2020 ◽  
Vol 2020 ◽  
pp. 1-9
Author(s):  
Anchit Bijalwan
Keyword(s):  
Machine Learning ◽  
Ensemble Classifier ◽  
Forensic Analysis ◽  
Modus Operandi ◽  
Botnet Detection ◽  
Quality Of Results ◽  
Proposed Model ◽  
Improve Accuracy ◽  
Rapid Pace

Botnet forensic analysis helps in understanding the nature of attacks and the modus operandi used by the attackers. Botnet attacks are difficult to trace because of their rapid pace, epidemic nature, and smaller size. Machine learning works as a panacea for botnet attack related issues. It not only facilitates detection but also helps in prevention from bot attack. The proposed inquisition model endeavors improved quality of results by comprehensive botnet detection and forensic analysis. This scenario has been applied in eight different combinations of ensemble classifier technique to detect botnet evidence. The study is also compared to the ensemble-based classifiers with the single classifier using different parameters. The results exhibit that the proposed model can improve accuracy over a single classifier.

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