Query: Pin Accessibility prediction and optimization with deep learning-based

Candidate papers:

abstract: Since the process node of semiconductor keeps scaling down, standard cells become much smaller and cell counts are dramatically increased. Standard cells on the lower metal layers severely suffer from low routability due to high pin density, low pin accessibility, and limited routing resources. Fig. 1 gives an example of the bad pin accessibility of cell C2 affected by the adjacent cell C1. It can be observed that the access points of pin B are blocked by the metal 2 (M2) routing segments routed from Pin A and Pin C, so an M2 short design rule violation (DRV) will be induced when dropping a via12 on Pin B. This example shows that pin accessibility is not only determined by cell layout design but also strongly affected by adjacent cells.
author_names: Tao-Chun Yu

Shao-Yun Fang

Hsien-Shih Chiu

Kai-Shun Hu

Philip Hui-Yuh Tai

Cindy Chin-Fang Shen

Henry Sheng
title: Pin Accessibility Prediction and Optimization with Deep Learning based Pin Pattern Recognition*
venue: 2019 56th ACM/IEEE Design Automation Conference (DAC)
year: 2019

abstract: Machine learning techniques based on neural networks are achieving remarkable results in a wide variety of domains. Often, the training of models requires large, representative datasets, which may be crowdsourced and contain sensitive information. The models should not expose private information in these datasets. Addressing this goal, we develop new algorithmic techniques for learning and a refined analysis of privacy costs within the framework of differential privacy. Our implementation and experiments demonstrate that we can train deep neural networks with non convex objectives, under a modest privacy budget, and at a manageable cost in software complexity, training efficiency, and model quality.
author_names: Martin Abadi

Andy Chu

Ian J Goodfellow

H Brendan McMahan

Ilya Mironov

Kunal Talwar

Li Zhang
title: Deep Learning with Differential Privacy
venue: CCS
year: 2016

abstract: A new efficient optimization method, called 'Teaching Learning Based Optimization (TLBO) is proposed in this paper for the optimization of mechanical design problems. This method works on the effect of influence of a teacher on learners. Like other nature inspired algorithms, TLBO is also a population based method and uses a population of solutions to proceed to the global solution. The population is considered as a group of learners or a class of learners. The process of TLBO is divided into two parts: the first part consists of the 'Teacher Phase' and the second part consists of the 'Learner Phase' 'Teacher Phase' means learning from the teacher and 'Learner Phase' means learning by the interaction between learners. The basic philosophy of the TLBO method is explained in detail. To check the effectiveness of the method it is tested on five different constrained benchmark test functions with different characteristics, four different benchmark mechanical design problems and six mechanical design optimization problems which have real world applications. The effectiveness of the TLBO method is compared with the other population based optimization algorithms based on the best solution, average solution, convergence rate and computational effort. Results show that TLBO is more effective and efficient than the other optimization methods for the mechanical design optimization problems considered. This novel optimization method can be easily extended to other engineering design optimization problems.
author_names: Ravipudi Venkata Rao

Vimal J Savsani

D P Vakharia
title: Teaching learning based optimization: A novel method for constrained mechanical design optimization problems
venue: Comput. Aided Des.
year: 2011

abstract: From the Publisher: This book brings together in an informal and tutorial fashion the computer techniques, mathematical tools, and research results that will enable both students and practitioners to apply genetic algorithms to problems in many fields. Major concepts are illustrated with running examples, and major algorithms are illustrated by Pascal computer programs. No prior knowledge of GAs or genetics is assumed, and only a minimum of computer programming and mathematics background is required.
author_names: David E Goldberg
title: Genetic Algorithms in Search Optimization and Machine Learning
year: 1988

abstract: Robust and powerful software instrumentation tools are essential for program analysis tasks such as profiling, performance evaluation, and bug detection. To meet this need, we have developed a new instrumentation system called Pin. Our goals are to provide easy to use, portable, transparent, and efficient instrumentation. Instrumentation tools (called Pintools) are written in C/C+ using Pin's rich API. Pin follows the model of ATOM, allowing the tool writer to analyze an application at the instruction level without the need for detailed knowledge of the underlying instruction set. The API is designed to be architecture independent whenever possible, making Pintools source compatible across different architectures. However, a Pintool can access architecture specific details when necessary. Instrumentation with Pin is mostly transparent as the application and Pintool observe the application's original, uninstrumented behavior. Pin uses dynamic compilation to instrument executables while they are running. For efficiency, Pin uses several techniques, including inlining, register re allocation, liveness analysis, and instruction scheduling to optimize instrumentation. This fully automated approach delivers significantly better instrumentation performance than similar tools. For example, Pin is 3.3x faster than Valgrind and 2x faster than DynamoRIO for basic block counting. To illustrate Pin's versatility, we describe two Pintools in daily use to analyze production software. Pin is publicly available for Linux platforms on four architectures: IA32 (32 bit x86) EM64T (64 bit x86) Itanium(r) and ARM. In the ten months since Pin 2 was released in July 2004, there have been over 3000 downloads from its website.
author_names: C K Luk

Robert S Cohn

Robert Muth

Harish Patil

Artur Klauser

G Lowney

Steven Wallace

Vijay Janapa Reddi

Kim M Hazelwood
title: Pin: building customized program analysis tools with dynamic instrumentation
venue: PLDI '05
year: 2005

abstract: The use of machine learning algorithms frequently involves careful tuning of learning parameters and model hyperparameters. Unfortunately, this tuning is often a "black art" requiring expert experience, rules of thumb, or sometimes brute force search. There is therefore great appeal for automatic approaches that can optimize the performance of any given learning algorithm to the problem at hand. In this work, we consider this problem through the framework of Bayesian optimization, in which a learning algorithm's generalization performance is modeled as a sample from a Gaussian process (GP) We show that certain choices for the nature of the GP, such as the type of kernel and the treatment of its hyperparameters, can play a crucial role in obtaining a good optimizer that can achieve expertlevel performance. We describe new algorithms that take into account the variable cost (duration) of learning algorithm experiments and that can leverage the presence of multiple cores for parallel experimentation. We show that these proposed algorithms improve on previous automatic procedures and can reach or surpass human expert level optimization for many algorithms including latent Dirichlet allocation, structured SVMs and convolutional neural networks.
author_names: Jasper Snoek

H Larochelle

Ryan P Adams
title: Practical Bayesian Optimization of Machine Learning Algorithms
venue: NIPS
year: 2012

abstract: Deeper neural networks are more difficult to train. We present a residual learning framework to ease the training of networks that are substantially deeper than those used previously. We explicitly reformulate the layers as learning residual functions with reference to the layer inputs, instead of learning unreferenced functions. We provide comprehensive empirical evidence showing that these residual networks are easier to optimize, and can gain accuracy from considerably increased depth. On the ImageNet dataset we evaluate residual nets with a depth of up to 152 layers 8x deeper than VGG nets [40] but still having lower complexity. An ensemble of these residual nets achieves 3.57% error on the ImageNet test set. This result won the 1st place on the ILSVRC 2015 classification task. We also present analysis on CIFAR 10 with 100 and 1000 layers. The depth of representations is of central importance for many visual recognition tasks. Solely due to our extremely deep representations, we obtain a 28% relative improvement on the COCO object detection dataset. Deep residual nets are foundations of our submissions to ILSVRC COCO 2015 competitions1, where we also won the 1st places on the tasks of ImageNet detection, ImageNet localization, COCO detection, and COCO segmentation.
author_names: Kaiming He

X Zhang

Shaoqing Ren

Jian Sun
title: Deep Residual Learning for Image Recognition
venue: 2016 IEEE Conference on Computer Vision and Pattern Recognition (CVPR)
year: 2016

abstract: We explore an original strategy for building deep networks, based on stacking layers of denoising autoencoders which are trained locally to denoise corrupted versions of their inputs. The resulting algorithm is a straightforward variation on the stacking of ordinary autoencoders. It is however shown on a benchmark of classification problems to yield significantly lower classification error, thus bridging the performance gap with deep belief networks (DBN) and in several cases surpassing it. Higher level representations learnt in this purely unsupervised fashion also help boost the performance of subsequent SVM classifiers. Qualitative experiments show that, contrary to ordinary autoencoders, denoising autoencoders are able to learn Gabor like edge detectors from natural image patches and larger stroke detectors from digit images. This work clearly establishes the value of using a denoising criterion as a tractable unsupervised objective to guide the learning of useful higher level representations.
author_names: Pascal Vincent

H Larochelle

Isabelle Lajoie

Yoshua Bengio

Pierre-Antoine Manzagol
title: Stacked Denoising Autoencoders: Learning Useful Representations in a Deep Network with a Local Denoising Criterion
venue: J. Mach. Learn. Res.
year: 2010

abstract: Functional annotation of proteins is a fundamental problem in the post genomic era. The recent availability of protein interaction networks for many model species has spurred on the development of computational methods for interpreting such data in order to elucidate protein function. In this review, we describe the current computational approaches for the task, including direct methods, which propagate functional information through the network, and module assisted methods, which infer functional modules within the network and use those for the annotation task. Although a broad variety of interesting approaches has been developed, further progress in the field will depend on systematic evaluation of the methods and their dissemination in the biological community.
author_names: Roded Sharan

Igor Ulitsky

Ron Shamir
title: Network based prediction of protein function
venue: Molecular systems biology
year: 2007

abstract: Modern mobile devices have access to a wealth of data suitable for learning models, which in turn can greatly improve the user experience on the device. For example, language models can improve speech recognition and text entry, and image models can automatically select good photos. However, this rich data is often privacy sensitive, large in quantity, or both, which may preclude logging to the data center and training there using conventional approaches. We advocate an alternative that leaves the training data distributed on the mobile devices, and learns a shared model by aggregating locally computed updates. We term this decentralized approach Federated Learning. We present a practical method for the federated learning of deep networks based on iterative model averaging, and conduct an extensive empirical evaluation, considering five different model architectures and four datasets. These experiments demonstrate the approach is robust to the unbalanced and non IID data distributions that are a defining characteristic of this setting. Communication costs are the principal constraint, and we show a reduction in required communication rounds by 10 100x as compared to synchronized stochastic gradient descent.
author_names: H Brendan McMahan

Eider Moore

Daniel Ramage

Seth Hampson

Blaise Aguera y Arcas
title: Communication Efficient Learning of Deep Networks from Decentralized Data
venue: AISTATS
year: 2017