• Journal of the Institute of Electronics Engineers of Korea CI
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• v.41 no.6
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• pp.1-11
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• 2004

Modern processors achieve high performance exploiting avaliable Instruction Level Parallelism(ILP) by using speculative technique such as branch prediction. Traditionally, branch direction can be predicted at very high accuracy by 2-level predictor, and branch target address is predicted by Branch Target Buffer(BTB). Except for indirect branch, each of the branch has the unique target, so its prediction is very accurate via BTB. But because indirect branch has dynamically polymorphic target, indirect branch target prediction is very difficult. In general, the technique of branch direction prediction is applied to indirect branch target prediction, and much better accuracy than traditional BTB is obtained for indirect branch. We present a new indirect branch target prediction scheme which combines a indirect branch instruction with its data dependent register of the instruction executed earlier than the branch. The result of SPEC benchmark simulation which are obtained on SimpleScalar simulator shows that the proposed predictor obtains the most perfect prediction accuracy than any other existing scheme.

• Proceedings of the IEEK Conference
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• 2005.11a
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• pp.1083-1086
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• 2005

Modern superscalar processors exploit Instruction Level Parallelism to achieve high performance by speculative techniques such as branch prediction. The indirect branch target prediction is very difficult compared to the prediction of direct branch target and branch direction, since it has dynamically polymorphic target. We present a accurate and hardware-efficient indirect branch target predictor. It can reduce the tags which has to be stored in the Indirect Branch Target Cache without a sacrifice of the prediction accuracy. We implement the proposed scheme on SimpleScalar and show the efficiency running SPEC95 benchmarks.

• Journal of the Institute of Electronics Engineers of Korea CI
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• v.43 no.4 s.310
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• pp.1-14
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• 2006

The indirect branch instruction is a most substantial obstacle in utilizing ILP of modem high performance processors. The target address of an indirect branch has the polymorphic characteristic varied dynamically, so it is very difficult to predict the accurate target address. Therefore the performance of a processor with speculative methodology is reduced significantly due to the many execution cycle delays in occurring the misprediction. We proposed the very accurate and novel indirect branch prediction scheme so called data-dependence based prediction. The predictor results in the prediction accuracy of 98.92% using 1K entries, and. 99.95% using 8K But, all of the proposed indirect predictor including our predictor has a large hardware overhead for restoring expected target addresses as well as tags for alleviating an aliasing. Hence, we propose the scheme minimizing the hardware overhead without sacrificing the prediction accuracy. Our experiment results show that the hardware is reduced about 60% without the performance loss, and about 80% sacrificing only the performance loss of 0.1% in aspect of the tag overhead. Also, in aspect of the overhead of storing target addresses, it can save the hardware about 35% without the performance loss, and about 45% sacrificing only the performance loss of 1.11%.

• Proceedings of the Korean Information Science Society Conference
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• 2011.06a
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• pp.494-497
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• 2011

함수 복귀 예측은 이론적으로 오버플로가 발생하지 않는 한도 내에서 100%의 정확도를 보여야 한다. 하지만, 투기적 실행을 지원하는 현대 마이크로프로세서 환경 하에서는 잘못된 실행 경로로의 수행 결과를 무효화 할 때 RAS의 오염이 발생하며, 이는 함수 복귀 주소의 예측 실패로 이어진다. 본 논문에서는 이러한 RAS의 오염을 방지하기 위하여 RAS 재명명 기법을 제안한다. RAS 재명명 기법은 RAS의 스택을 소프트 스택과 하드 스택으로 나누어 관리한다. 소프트 스택은 투기적 실행에 의한 데이터의 변경을 복구할 수 있는 항목을 관리하고, 하드 스택은 소프트 스택의 크기 제한으로 겹쳐쓰기가 일어나는 데이터 가운데 이후에 재사용될 데이터를 관리하는 구조로 구성된다. 제안된 기법을 모의실험 한 결과, RAS 오염방지 기법이 적용되지 않은 시스템과 비교하여 함수 복귀 예측 실패를 약 1/90로 감소시켰으며, 최대 6.95%의 IPC 향상을 가져왔다.

• Geomechanics and Engineering
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• v.22 no.5
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• pp.397-414
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• 2020

In this paper, practical predictive models for soil shear strength parameters are proposed. As cohesion and internal friction angle are of essential shear strength parameters in any geotechnical studies, we try to predict them via artificial neural network (ANN) and neuro-imperialism approaches. The proposed models was based on the result of a series of consolidated undrained triaxial tests were conducted on reinforced sandy soil. The experimental program surveys the increase in internal friction angle of sandy soil due to addition of polypropylene fibers with different lengths and percentages. According to the result of the experimental study, the most important parameters impact on internal friction angle i.e., fiber percentage, fiber length, deviator stress, and pore water pressure were selected as predictive model inputs. The inputs were used to construct several ANN and neuro-imperialism models and a series of statistical indices were calculated to evaluate the prediction accuracy of the developed models. Both simulation results and the values of computed indices confirm that the newly-proposed neuro-imperialism model performs noticeably better comparing to the proposed ANN model. While neuro-imperialism model has training and test error values of 0.068 and 0.094, respectively, ANN model give error values of 0.083 for training sets and 0.26 for testing sets. Therefore, the neuro-imperialism can provide a new applicable model to effectively predict the internal friction angle of fiber-reinforced sandy soil.