DeepLearningEE

KO | EN

gitlite — search

#javascript #python #react #typescript #android #docker #nodejs #windows #awesome #cli #kubernetes #awesome-list #security #ios

(★ 46)

Collection of Papers and Trials on Deep Learning to aid EE design

DeepLearningEE Latest Version Download

Download Latest Version (.zip)

// repository documentation

Was this content helpful?

★ 0 (0 ratings)

# DeepLearningEE
Collection of Papers on Deep Learning to aid EE design

## Birds View
[Jeff Dean: The Deep Learning Revolution and Its Implications for Computer Architecture and Chip Design](https://arxiv.org/ftp/arxiv/papers/1911/1911.05289.pdf)::[ZDNet Article](https://www.zdnet.com/article/google-experiments-with-ai-to-design-its-in-house-computer-chips/)

[Survey of DL for Scientific Discovery(2020)](https://scholar.google.com/scholar?hl=en&as_sdt=2005&sciodt=0%2C5&cites=1635875132167322453&scipsc=&q=A+survey+of+deep+learning+for+scientific+discovery&btnG=)

[Tech. Report : End-to-End DRL in Computer Systems(2020)](https://scholar.google.com/scholar?hl=en&as_sdt=2005&sciodt=0%2C5&cites=6073538280506520373&scipsc=&q=End-to-end+deep+reinforcement+learning+in+computer+systems&btnG=)

[Machine Learning for Engineering(2018)](https://scholar.google.com/scholar?hl=en&as_sdt=0%2C5&as_ylo=2016&q=machine+learning+for+engineering+solido&btnG=)

[Thesis: New Applications of Learning-Based Modeling in Nanoscale Integrated-Circuit Design(2016)](https://scholar.google.com/scholar?hl=en&as_sdt=0%2C5&q=New+Applications+of+Learning-Based+Modeling+in+Nanoscale+Integrated-Circuit+Design&btnG=)

[Thesis: Low Power EDA(2006)](https://scholar.google.com/scholar?hl=en&as_sdt=0%2C5&q=Low+Power+Design+Automation+chinnery&btnG=)

## Data
[ISPD](http://www.ispd.cc/contests/19/)

## Metrics
[Metrics 2.0](https://woset-workshop.github.io/PDFs/a21.pdf)

## Tools
[OpenRoad](https://github.com/The-OpenROAD-Project) :: [Paper](https://scholar.google.com/scholar?hl=en&as_sdt=0%2C5&q=OpenROAD%3A+Toward+a+Self-Driving%2C+Open-SourceDigital+Layout+Implementation+Tool+Chain&btnG=)

[Park Platform for Reinforcement Learning in Systems](https://scholar.google.com/scholar?hl=en&as_sdt=0%2C5&q=Park+platform+reinforcement+learning+Mao&btnG=)

[Quantum Circuit Optimization](https://github.com/google-research/google-research/tree/master/rl4circopt)

[ChipKit: Agile Open Src Framework](https://scholar.google.com/scholar?hl=en&as_sdt=0%2C5&q=CHIPKIT%3A+An+agile%2C+reusable+open-source+framework+for+rapid+test+chip+development&btnG=)

## Algorithms
### Heuristic Algorithms
[Reinforcement Learning + Heuristics(2019)](https://scholar.google.com/scholar?hl=en&as_sdt=0%2C5&q=Reinforcement+Learning+Driven+Heuristic+Optimization&btnG=)

### Graph Algorithms
[Understanding Graphs in EDA](https://scholar.google.com/scholar?hl=en&as_sdt=0%2C5&q=Understanding+Graphs+in+EDA%3A+From+Shallow+to+Deep+Learning&btnG=)

[Generalized Clustering by Learning to Optimize Expected Normalized Cuts](https://scholar.google.com/scholar?hl=en&as_sdt=0%2C5&q=Generalized+Clustering+by+Learning+to+Optimize+Expected+Normalized+Cuts&btnG=)

[GAP Partitioning Framework](https://scholar.google.com/scholar?hl=en&as_sdt=0%2C5&q=gap+generalizable+partitioning+framework&btnG=) :: [Deep Learning Framework for Graph Partitioning(GAP)(2019)](https://rlgm.github.io/papers/41.pdf)

### Clustering
[Generalized Clustering to Optimize Cuts](https://openreview.net/forum?id=BklLVAEKvH) :::: [Code](https://github.com/google-research/google-research/tree/master/clustering_normalized_cuts)

## Simulator Speed Up
[Billion Times](https://www.sciencemag.org/news/2020/02/models-galaxies-atoms-simple-ai-shortcuts-speed-simulations-billions-times) :: [Paper](https://scholar.google.com/scholar?hl=en&as_sdt=0%2C5&q=Up+to+two+billion+times+acceleration+of+scientific+simulationswith+deep+neural+architecture+search&btnG=)

[Circuit Simulation Parallelization](https://scholar.google.com/scholar?hl=en&as_sdt=0%2C5&q=NN-PARS%3A+A+Parallelized+Neural+Network+BasedCircuit+Simulation+Framework&btnG=)

[Coping with Envions with Slow Simulator and Fast Approximations](https://scholar.google.com/scholar?hl=en&as_sdt=2005&sciodt=0%2C5&cites=16968406362095427294&scipsc=&q=An+Agent-Training+Framework+for+Coping+with+Environments+with+Slow+Simulators+that+have+Fast+Approximations&btnG=)

## Analog Design
[Agent Training Framework For Analog Design](https://scholar.google.com/scholar?hl=en&as_sdt=2005&sciodt=0%2C5&cites=756184528877571987&scipsc=&q=An+Agent-Training+Framework+for+Coping+with+Environmentswith+Slow+Simulators+that+have+Fast+Approximations&btnG=)

[AutoCkt: Deep Reinforcement Learning of AnalogCircuit Designs](https://scholar.google.com/scholar?hl=en&as_sdt=0%2C5&q=AutoCkt%3A+Deep+Reinforcement+Learning+of+AnalogCircuit+Designs&btnG=)

[Bagnet(2019)](https://scholar.google.com/scholar?hl=en&as_sdt=0%2C5&q=bagnet+berkeley+analog&btnG=)

[Learning to Design Circuits(2018)](https://scholar.google.com/scholar?hl=en&as_sdt=0%2C5&q=%22learning+to+design+circuits%22&btnG=&oq=learning+to+desig)

[Magical](https://scholar.google.com/scholar?hl=en&as_sdt=0%2C5&q=MAGICAL%3A+Toward+Fully+Automated+Analog+ICLayout+Leveraging+Human+and+Machine+Intelligence&btnG=)

[Differential Simulator](https://scholar.google.com/scholar?hl=en&as_sdt=0%2C5&q=ELECTRIC+ANALOG+CIRCUIT+DESIGN+WITH+HYPERNETWORKS+AND+A+DIFFERENTIAL+SIMULATOR&btnG=)

[GCN-RL](https://scholar.google.com/scholar?hl=en&as_sdt=2005&sciodt=0%2C5&cites=9647065262913064608&scipsc=&q=GCN-RL+Circuit+Designer%3A+Transferable+Transistor+Sizing+with+Graph+Neural+Networks+and+Reinforcement+Learning&btnG=)

[Placement Quality Prediction using Transfer Learning](https://scholar.google.com/scholar?hl=en&as_sdt=2005&sciodt=0%2C5&cites=763066188181261234&scipsc=&q=Towards+Decrypting+the+Art+of+Analog+Layout%3A+Placement+Quality+Prediction+via+Transfer+Learning&btnG=)

## Digital Design

### Standard Cells
[Generating power-optimal std cell lib spec using NN(2017)](https://scholar.google.com/scholar?hl=en&as_sdt=0%2C5&q=+Generating+power-optimal+standard+cell+library+specification+using+neural+network+technique+&btnG=)

### DFT
[GCN for Testability Analysis](https://scholar.google.com/scholar?q=High+Performance+Graph+Convolutional+Networks+with+Applications+in+Testability+Analysis&hl=en&as_sdt=0&as_vis=1&oi=scholart)

### HLS
[AutoPhase: Phase Ordering for HLS using DRL(2019)](https://scholar.google.com/scholar?hl=en&as_sdt=0,5&q=autophase+high+level+synthesis)

### Synthesis
[New Full Adders using MLP(2020)](https://scholar.google.com/scholar?hl=en&as_sdt=2005&sciodt=0%2C5&cites=18056219889831816110&scipsc=&q=New+full+adders+using+multi-layer+perceptron+network&btnG=)

[LSOracle: Synthesis Framework Driven by AI(2019)](https://scholar.google.com/scholar?hl=en&as_sdt=2005&sciodt=0%2C5&cites=18056219889831816110&scipsc=&q=+LSOracle%3A+a+Logic+Synthesis+Framework+Driven+by+Artificial+Intelligence%3A+Invited+Paper+&btnG=)

[DL based Ckt Recognition using sparse mapping(2019)](https://scholar.google.com/scholar?hl=en&as_sdt=0%2C5&q=deep+learning+based+circuit+recognition+using+sparse+mapping&btnG=)

[Deep-PowerX Low Power Approximation Synthesis](https://arxiv.org/pdf/2007.01465.pdf)

[Improving SAT Solvers(2019)](https://scholar.google.com/scholar?q=Improving+SAT+Solver+Heuristics+with+Graph+Networks+and+Reinforcement+Learning&hl=en&as_sdt=0&as_vis=1&oi=scholart)

[Approximate Logic Synth Using Reinforcement Learning(2019)](https://scholar.google.com/scholar?hl=en&as_sdt=0%2C5&q=Approximate+Logic+Synthesis%3A+A+ReinforcementLearning-Based+Technology+Mapping+Approach&btnG=)

[Perf. Esti of Synth Flows using LSTM & Transfer Learning(2018)](https://scholar.google.com/scholar?hl=en&as_sdt=2005&sciodt=0%2C5&cites=6073538280506520373&scipsc=&q=performance+estimation+of+synthesis+flows+using+LSTM&btnG=)

[Cadence: A Learning Bridge from Arch. Synthesis to PD for Exploring Power Efficient High-Performance Adders(2017)](https://scholar.google.com/scholar?hl=en&as_sdt=0%2C5&q=A+Learning+Bridge+from+Architectural+Synthesis+to+PhysicalDesign+for+Exploring+Power+Efficient+High-Performance+Adde&btnG=)

[Cadence, Intel: Cross-layer Optimization for High Speed Adders:A Pareto Driven Machine Learning Approach(2018)](https://scholar.google.com/scholar?hl=en&as_sdt=0%2C5&q=Cross-layer+Optimization+for+High+Speed+Adders%3AA+Pareto+Driven+Machine+Learning+Approach&btnG=)

### Power Grid + Integrity
[PowerPlanningDL: Reliability-Aware Framework for PGD using DL(2020)](https://scholar.google.com/scholar?hl=en&as_sdt=0%2C5&q=PowerPlanningDL%3A+Reliability-Aware+Framework+forOn-Chip+Power+Grid+Design+using+Deep+Learning&btnG=)

[Template-based PDN Synthesis in Floorplan and PlacementUsing Classifier and CNN Techniques(2020)](https://scholar.google.com/scholar?hl=en&as_sdt=0%2C5&q=Template-based+PDN+Synthesis+in+Floorplan+and+PlacementUsing+Classifier+and+CNN+Techniques&btnG=)

[IncPIRD: Fast Learning-Based Prediction of Incremental IR Drop(2019)](https://scholar.google.com/scholar?hl=en&as_sdt=0%2C5&q=IncPIRD%3A+Fast+Learning-Based+Prediction+of+Incremental+IR+Drop&btnG=)

[IR-aware Power Net Routing for Multi-Voltage Mixed-Signal Design(2019)](https://scholar.google.com/scholar?hl=en&as_sdt=0%2C5&q=IR-aware+Power+Net+Routing+for+Multi-VoltageMixed-Signal+Design&btnG=)

[Generating Routing Driven Power Distribution n/w using ML Techniques(2017)](https://scholar.google.com/scholar?hl=en&as_sdt=0%2C5&as_ylo=2016&q=generating+routing+driven+power+distribution+networks&btnG=)

[PDN Simplifier(2018)](https://scholar.google.com/scholar?hl=en&as_sdt=0%2C5&q=+An+Approach+for+PDN+Simplification+of+a+Mobile+Processor+&btnG=)

[Fast PI Classifier](https://scholar.google.com/scholar?hl=en&as_sdt=0%2C5&q=machine+learning+based+fast+power+integrity+classifier&btnG=)

### Floorplan
[Solving Packing Problems by Conditional Query Learning(2020)](https://scholar.google.com/scholar?hl=en&as_sdt=2005&sciodt=0%2C5&cites=17171504097257349537&scipsc=&q=Solving+Packing+Problems+by+Conditional+Query+Learning&btnG=)

[Routability Driven Macro Placement](https://scholar.google.com/scholar?hl=en&as_sdt=0%2C5&q=Routability+Driven+Macro+Placement+nvidia&btnG=)

[Memory Controller Placement(2019)](https://iehttps://scholar.google.com/scholar?hl=en&as_sdt=0%2C5&as_ylo=2019&q=+Design+Space+Exploration+of+Memory+Controller+Placement+in+Throughput+Processors+with+Deep+Learning+&btnG=eexplore.ieee.org/document/8668490)

[NOC Component Charecterization](https://www.arteris.com/blog/arteris-ip-at-synopsys-users-group-silicon-valley-2019)

### Placement
[Chip Placement with DRL](https://scholar.google.com/scholar?hl=en&as_sdt=0%2C5&q=Chip+Placement+with+Deep+Reinforcement+Learning&btnG=)

[Placement Optimization using DRL](https://scholar.google.com/scholar?q=Placement+Optimization+with+Deep+Reinforcement+Learning&hl=en&as_sdt=0&as_vis=1&oi=scholart)

[DreamPlace 2](https://scholar.google.com/scholar?hl=en&as_sdt=0%2C5&q=DREAMPlace+2.0%3A+Open-Source+GPU-Accelerated+Global+and+Detailed+Placement+for+Large-Scale+VLSI+Designs&btnG=)

[Legalization(2019)](https://scholar.google.com/scholar?hl=en&as_sdt=2005&sciodt=0%2C5&cites=17370600981447306812&scipsc=&q=How+Deep+Learning+Can+Drive+Physical+SynthesisTowards+More+Predictable+Legalization&btnG=)
[Presentation](http://www.ispd.cc/slides/2019/1_placement_HowDeep.pdf)

### CTS
[GAN CTS](https://scholar.google.com/scholar?hl=en&as_sdt=0%2C5&q=GAN-CTS%3A+A+Generative+Adversarial+Frameworkfor+Clock+Tree+Prediction+and+Optimization&btnG=)

### Routing
[Cadence - Attention Based RL for Track Assign + Detail Routing(2020)](https://scholar.google.com/scholar?hl=en&as_sdt=0%2C5&q=Attention+Routing%3A+track-assignment+detailed+routing+using+attention-based+reinforcement+learning&btnG=)

[Nvidia's CongestionNet](https://scholar.google.com/scholar?hl=en&as_sdt=2005&sciodt=0%2C5&cites=3871127596313851353&scipsc=&q=+CongestionNet%3A+Routing+Congestion+Prediction+Using+Deep+Graph+Neural+Networks+&btnG=)

[Deep Reinforcement Learning for Global Routing](https://scholar.google.com/scholar?hl=en&as_sdt=0%2C5&q=A+DEEP+REINFORCEMENT+LEARNINGAPPROACH+FOR+GLOBAL+ROUTING&btnG=)

[Congestion Prediction using GANs](https://scholar.google.com/scholar?hl=en&as_sdt=0%2C5&q=High-Definition+Routing+Congestion+Prediction+for+Large+Scale+FPGAs&btnG=)

[Congestion Prediction using Conditional GANs](https://scholar.google.com/scholar?hl=en&as_sdt=0%2C5&q=Painting+on+Placement%3A+Forecasting+Routing+Congestion+usingConditional+Generative+Adversarial+Nets&btnG=)

[RouteNet(2018)](https://scholar.google.com/scholar?hl=en&as_sdt=2005&sciodt=0%2C5&cites=2856161563400953511&scipsc=&q=RouteNet%3A+Routability+Prediction+for+Mixed-Size+Designs+UsingConvolutional+Neural+Network&btnG=)

[Pin Accessibility Prediction](https://scholar.google.com/scholar?hl=en&as_sdt=0%2C5&q=Pin+accessibility+prediction+and+optimization+with+deep+learning-based+pin+pattern+recognition&btnG=)

[Ckt Routing using Monte Carlo Tree Search and DL](https://scholar.google.com/scholar?hl=en&as_sdt=0%2C5&q=Circuit+Routing+Using+Monte+Carlo+Tree+Search+and+Deep+Neural+Networks&btnG=)

### Route Coloring
[Coloring: Adaptive Layout Decomposition](https://scholar.google.com/scholar?q=Adaptive+Layout+Decomposition+with+Graph+Embedding+Neural+Networks&hl=en&as_sdt=0&as_vis=1&oi=scholart)

[DL Driven Decompostion & Mask Optimization](https://scholar.google.com/scholar?hl=en&as_sdt=0%2C5&q=Deep+Learning-Driven+Simultaneous+Layout+Decomposition+and+Mask+Optimization&btnG=)

### Verification
[Deep Predictive Coverage Collection(Nvidia)](https://scholar.google.com/scholar?hl=en&as_sdt=2005&sciodt=0%2C5&cites=9658564771958204517&scipsc=&q=Deep+Predictive+Coverage+Collection&btnG=)

### STA
[CSM with NN](https://scholar.google.com/scholar?hl=en&as_sdt=2005&sciodt=0%2C5&cites=10619245689743073789&scipsc=&q=CSM-NN%3A+Current+Source+Model+Based+LogicCircuit+Simulation+-+A+Neural+Network+Approach&btnG=)

### Package
[Automatically Designed EBG](https://scholar.google.com/scholar?hl=en&as_sdt=2005&sciodt=0%2C5&cites=9848414990295400109&scipsc=&q=+A+Novel+Automatically+Designed+EBG+Structure+by+Improved+GA+for+Ultrawideband+SSN+Mitigation+of+System+in+Package+&btnG=)

### Reliability
[predict vlsi circuit reliability risks using neural network(2014)](https://scholar.google.com/scholar?hl=en&as_sdt=0%2C5&q=predict+vlsi+circuit+reliability+risks+using+neural+network&btnG=)

### Power Analysis
[Power Modeling and Estimation Survey 2020](https://scholar.google.com/scholar?hl=en&as_sdt=2005&sciodt=0%2C5&cites=15879332215242804597&scipsc=&q=RTL+to+Transistor+Level+Power+Modelling+and+Estimation+Techniques+for+FPGA+and+ASIC%3A+A+Survey&btnG=)

[Power Inference](https://scholar.google.com/scholar?hl=en&as_sdt=0%2C5&q=PRIMAL%3A+Power+Inference+using+Machine+Learning&btnG=)

### Variation Modeling
[Variation Modeling](https://www.synopsys.com/zh-tw/taiwan/events/2019-elite-snug.html)

[Automatic tuning of a quantum device](https://scholar.google.com/scholar?hl=en&as_sdt=0%2C5&q=Machine+learning+enables+completely+automatictuning+of+a+quantum+device+faster+than+humanexperts&btnG=)

### Manufacturing
[Defect Prediction in PCBs](https://scholar.google.com/scholar?hl=en&as_sdt=0%2C5&q=A+Deep+Learning+Framework+for+Simulation+and+DefectPrediction+Applied+in+Microelectronics&btnG=)

## Computer Architecture

### Resource Mngt
[Resource Management of Heterogenous SoCs](https://scholar.google.com/scholar?hl=en&as_sdt=0%2C5&q=Online+Adaptive+Learning+for+Runtime+Resource+Management+of+Heterogeneous+SoCs&btnG=)

### DVFS
[Data Driven DVFS Schedule for GPU](https://scholar.google.com/scholar?hl=en&as_sdt=0%2C5&q=A+Data-Driven+Frequency+Scaling+Approach+for+Deadline-aware+Energy+Efficient+Scheduling+on+Graphics+Processing+Units+&btnG=)

### Literature Survey
[Survey 2019](https://scholar.google.com/scholar?hl=en&as_sdt=2005&sciodt=0%2C5&cites=15879332215242804597&scipsc=&q=A+Survey+of+Machine+Learning+Applied+toComputer+Architecture+Design&btnG=)

### Prefetch
[Classifying Memory Access patterns for Prefetching](https://scholar.google.com/scholar?hl=en&as_sdt=0%2C5&q=Understanding+Memory+Access+Patterns+for+Prefetching&btnG=)

[LSTM Based Prefetcher Thesis 2017](https://scholar.google.com/scholar?hl=en&as_sdt=0%2C5&q=long+short+term+based+memory+hardware+prefetcher&btnG=)

### Cache Replacement
[Imitation Learning for Cache Replacement](https://scholar.google.com/scholar?q=An+Imitation+Learning+Approach+for+Cache+Replacement+Show+affiliations&hl=en&as_sdt=0&as_vis=1&oi=scholart)

## Power Modeling
[Learning based Power Modelling](https://scholar.google.com/scholar?hl=en&as_sdt=0%2C5&q=Learning-Based+CPU+Power+Modeling+ajay+krishna&btnG=)

DeepLearningEE

DeepLearningEE Latest Version Download

Today's picks

Was this content helpful?

Recent Feedback

DeepLearningEE

DeepLearningEE Latest Version Download

Today's picks

Was this content helpful?

Recent Feedback

Download README