Outline Motivation and Contributions Related Works ILP Formulation

Slides:



Advertisements
Similar presentations
I/O Placement for FPGAs with Multiple I/O Standards.
Advertisements

OCV-Aware Top-Level Clock Tree Optimization
Timing Margin Recovery With Flexible Flip-Flop Timing Model
Minimum Implant Area-Aware Gate Sizing and Placement
1 Advancing Supercomputer Performance Through Interconnection Topology Synthesis Yi Zhu, Michael Taylor, Scott B. Baden and Chung-Kuan Cheng Department.
Meng-Kai Hsu, Sheng Chou, Tzu-Hen Lin, and Yao-Wen Chang Electronics Engineering, National Taiwan University Routability Driven Analytical Placement for.
Multi-Project Reticle Floorplanning and Wafer Dicing Andrew B. Kahng 1 Ion I. Mandoiu 2 Qinke Wang 1 Xu Xu 1 Alex Zelikovsky 3 (1) CSE Department, University.
Background: Scan-Based Delay Fault Testing Sequentially apply initialization, launch test vector pairs that differ by 1-bit shift A vector pair induces.
Evaluation of Placement Techniques for DNA Probe Array Layout Andrew B. Kahng 1 Ion I. Mandoiu 2 Sherief Reda 1 Xu Xu 1 Alex Zelikovsky 3 (1) CSE Department,
Faster SAT and Smaller BDDs via Common Function Structure Fadi A. Aloul, Igor L. Markov, Karem A. Sakallah University of Michigan.
Architectural-Level Prediction of Interconnect Wirelength and Fanout Kwangok Jeong, Andrew B. Kahng and Kambiz Samadi UCSD VLSI CAD Laboratory
Supply Voltage Degradation Aware Analytical Placement Andrew B. Kahng, Bao Liu and Qinke Wang UCSD CSE Department {abk, bliu,
Local Unidirectional Bias for Smooth Cutsize-delay Tradeoff in Performance-driven Partitioning Andrew B. Kahng and Xu Xu UCSD CSE and ECE Depts. Work supported.
Placement Feedback: A Concept and Method for Better Min-Cut Placements Andrew B. KahngSherief Reda CSE & ECE Departments University of CA, San Diego La.
On Legalization of Row-Based Placements Andrew B. KahngSherief Reda CSE & ECE Departments University of CA, San Diego La Jolla, CA 92093
Yield- and Cost-Driven Fracturing for Variable Shaped-Beam Mask Writing Andrew B. Kahng CSE and ECE Departments, UCSD Xu Xu CSE Department, UCSD Alex Zelikovsky.
Fast and Area-Efficient Phase Conflict Detection and Correction in Standard-Cell Layouts Charles Chiang, Synopsys Andrew B. Kahng, UC San Diego Subarna.
1 UCSD VLSI CAD Laboratory ISQED-2009 Revisiting the Linear Programming Framework for Leakage Power vs. Performance Optimization Kwangok Jeong, Andrew.
Can Recursive Bisection Alone Produce Routable Placements? Andrew E. Caldwell Andrew B. Kahng Igor L. Markov Supported by Cadence.
A Proposal for Routing-Based Timing-Driven Scan Chain Ordering Puneet Gupta 1 Andrew B. Kahng 1 Stefanus Mantik 2
Detailed Placement for Leakage Reduction Using Systematic Through-Pitch Variation Andrew B. Kahng †‡ Swamy Muddu ‡ Puneet Sharma ‡ CSE † and ECE ‡ Departments,
Thermal-Aware SoC Test Scheduling with Test Set Partitioning and Interleaving Zhiyuan He 1, Zebo Peng 1, Petru Eles 1 Paul Rosinger 2, Bashir M. Al-Hashimi.
1 Circuit Partitioning Presented by Jill. 2 Outline Introduction Cut-size driven circuit partitioning Multi-objective circuit partitioning Our approach.
UC San Diego Computer Engineering. VLSI CAD Laboratory.. UC San Diego Computer EngineeringVLSI CAD Laboratory.. UC San Diego Computer EngineeringVLSI CAD.
UC San Diego / VLSI CAD Laboratory Reliability-Constrained Die Stacking Order in 3DICs Under Manufacturing Variability Tuck-Boon Chan, Andrew B. Kahng,
Tabu Search-Based Synthesis of Dynamically Reconfigurable Digital Microfluidic Biochips Elena Maftei, Paul Pop, Jan Madsen Technical University of Denmark.
WISCAD – VLSI Design Automation GRIP: Scalable 3-D Global Routing using Integer Programming Tai-Hsuan Wu, Azadeh Davoodi Department of Electrical and Computer.
-1- UC San Diego / VLSI CAD Laboratory A Global-Local Optimization Framework for Simultaneous Multi-Mode Multi-Corner Clock Skew Variation Reduction Kwangsoo.
CSE 242A Integrated Circuit Layout Automation Lecture: Partitioning Winter 2009 Chung-Kuan Cheng.
1 SOC Test Architecture Optimization for Signal Integrity Faults on Core-External Interconnects Qiang Xu and Yubin Zhang Krishnendu Chakrabarty The Chinese.
Software Pipelining for Stream Programs on Resource Constrained Multi-core Architectures IEEE TRANSACTIONS ON PARALLEL AND DISTRIBUTED SYSTEM 2012 Authors:
Horizontal Benchmark Extension for Improved Assessment of Physical CAD Research Andrew B. Kahng, Hyein Lee and Jiajia Li UC San Diego VLSI CAD Laboratory.
Low-Power Gated Bus Synthesis for 3D IC via Rectilinear Shortest-Path Steiner Graph Chung-Kuan Cheng, Peng Du, Andrew B. Kahng, and Shih-Hung Weng UC San.
UC San Diego / VLSI CAD Laboratory Incremental Multiple-Scan Chain Ordering for ECO Flip-Flop Insertion Andrew B. Kahng, Ilgweon Kang and Siddhartha Nath.
An Efficient Clustering Algorithm For Low Power Clock Tree Synthesis Rupesh S. Shelar Enterprise Microprocessor Group Intel Corporation, Hillsboro, OR.
New Modeling Techniques for the Global Routing Problem Anthony Vannelli Department of Electrical and Computer Engineering University of Waterloo Waterloo,
1 CS612 Algorithms for Electronic Design Automation CS 612 – Lecture 8 Lecture 8 Network Flow Based Modeling Mustafa Ozdal Computer Engineering Department,
-1- UC San Diego / VLSI CAD Laboratory Construction of Realistic Gate Sizing Benchmarks With Known Optimal Solutions Andrew B. Kahng, Seokhyeong Kang VLSI.
1 Towards Optimal Custom Instruction Processors Wayne Luk Kubilay Atasu, Rob Dimond and Oskar Mencer Department of Computing Imperial College London HOT.
Kwangsoo Han, Andrew B. Kahng, Hyein Lee and Lutong Wang
Kwangsoo Han‡, Andrew B. Kahng‡† and Hyein Lee‡
Design of a High-Throughput Low-Power IS95 Viterbi Decoder Xun Liu Marios C. Papaefthymiou Advanced Computer Architecture Laboratory Electrical Engineering.
Resource Mapping and Scheduling for Heterogeneous Network Processor Systems Liang Yang, Tushar Gohad, Pavel Ghosh, Devesh Sinha, Arunabha Sen and Andrea.
Hsing-Chih Chang Chien Hung-Chih Ou Tung-Chieh Chen Ta-Yu Kuan Yao-Wen Chang Double Patterning Lithography-Aware Analog Placement.
Contingency-Constrained PMU Placement in Power Networks
6. A PPLICATION MAPPING 6.3 HW/SW partitioning 6.4 Mapping to heterogeneous multi-processors 1 6. Application mapping (part 2)
Optimality, Scalability and Stability study of Partitioning and Placement Algorithms Jason Cong, Michail Romesis, Min Xie UCLA Computer Science Department.
Data Structures and Algorithms in Parallel Computing Lecture 7.
1 CS612 Algorithms for Electronic Design Automation CS 612 – Lecture 8 Lecture 8 Network Flow Based Modeling Mustafa Ozdal Computer Engineering Department,
In-Place Decomposition for Robustness in FPGA Ju-Yueh Lee, Zhe Feng, and Lei He Electrical Engineering Dept., UCLA Presented by Ju-Yueh Lee Address comments.
Physically Aware HW/SW Partitioning for Reconfigurable Architectures with Partial Dynamic Reconfiguration Sudarshan Banarjee, Elaheh Bozorgzadeh, Nikil.
CprE566 / Fall 06 / Prepared by Chris ChuPartitioning1 CprE566 Partitioning.
Improved Path Clustering for Adaptive Path-Delay Testing Tuck-Boon Chan* and Prof. Andrew B. Kahng*# UC San Diego ECE* & CSE # Departments.
-1- UC San Diego / VLSI CAD Laboratory Optimization of Overdrive Signoff Tuck-Boon Chan, Andrew B. Kahng, Jiajia Li and Siddhartha Nath Tuck-Boon Chan,
Hypergraph Partitioning With Fixed Vertices Andrew E. Caldwell, Andrew B. Kahng and Igor L. Markov UCLA Computer Science Department
CAD for VLSI Ramakrishna Lecture#2.
Philip Brisk 2 Paolo Ienne 2 Hadi Parandeh-Afshar 1,2 1: University of Tehran, ECE Department 2: EPFL, School of Computer and Communication Sciences Improving.
-1- UC San Diego / VLSI CAD Laboratory Optimal Reliability-Constrained Overdrive Frequency Selection in Multicore Systems Andrew B. Kahng and Siddhartha.
Proximity Optimization for Adaptive Circuit Design Ang Lu, Hao He, and Jiang Hu.
1 Placement-Aware Architectural Synthesis of Digital Microfluidic Biochips using ILP Elena Maftei Institute of Informatics and Mathematical Modelling Technical.
Improved Flop Tray-Based Design Implementation for Power Reduction
Kun Young Chung*, Andrew B. Kahng+ and Jiajia Li+
Kristof Blutman† , Hamed Fatemi† , Andrew B
3Boston University ECE Dept.;
Improved Performance of 3DIC Implementations Through Inherent Awareness of Mix-and-Match Die Stacking Kwangsoo Han, Andrew B. Kahng and Jiajia Li University.
Andrew B. Kahng and Xu Xu UCSD CSE and ECE Depts.
Revisiting and Bounding the Benefit From 3D Integration
APLACE: A General and Extensible Large-Scale Placer
The use of Neural Networks to schedule flow-shop with dynamic job arrival ‘A Multi-Neural Network Learning for lot Sizing and Sequencing on a Flow-Shop’
Presentation transcript:

Co-optimization of Memory BIST Grouping, Test Scheduling, and Logic Placement Andrew B. Kahng and Ilgweon Kang VLSI CAD LABORATORY, UC San Diego Design, Automation & Test in Europe March 26th, 2014

Outline Motivation and Contributions Related Works ILP Formulation Heuristic Flow Experimental Results Conclusions and Future Work

Memory BIST Architecture Motivation Memory built-in self-test (MBIST) Essential DFT technique in modern SOCs MBIST design impacts chip resources and quality of test solution Physical optimizations of MBIST logic: not well-studied MEM BIST TAP Memory BIST Architecture

Challenges and Contributions Challenge: Multiple optimization criteria Minimize test time Minimize number of BIST controllers Minimize wirelength between BIST logic and memories Our work: Three-stage heuristic Memory partitioning : FM in a weighted hypergraph model Test scheduling : Integer Linear Program (ILP) formulation MBIST logic placement : min-weight maximum matching

Outline Motivation and Contributions Related Works ILP Formulation Heuristic Flow Experimental Results Conclusions and Future Work

Related Works Test scheduling Reduction of test time is a fundamental goal in DFT Typical: scheduling as rectangle packing Also typical: scheduling as ILP Design optimizations for memory BIST controllers Most works focus on architectural and testing aspects Chien et al. [2009] propose a memory BIST design optimization ILP for clustering of memories to controllers Studies physical design aspects Various simplifications: all of a cluster is tested in parallel, no two clusters tested simultaneously, …

Outline Motivation and Contributions Related Works ILP Formulation Heuristic Flow Experimental Results Conclusions and Future Work

Test time with one solution Improved test time is possible ILP Formulation Objective: Minimize test end time Subject to: Upper bound on test power Logical constraints capture parallel and serial testing scenarios power time MEM4 MEM2 MEM1 MEM3 MEM8 MEM6 MEM5 MEM9 MEM7 Test power limit Test time with one solution MBIST1 MBIST2 MEM3 power MEM8 MEM6 MEM5 MEM9 MEM7 MEM1 MEM2 MEM4 time Test power limit Improved test time is possible Reduced test time MBIST1 MBIST2

Outline Motivation and Contributions Related Works ILP Formulation Heuristic Flow Experimental Results Conclusions and Future Work

Overall Flow Input: Memories, Constraints Memory Partitioning with Hypergraph (MLPart [1], FM-style) (2) Test Scheduling with ILP formulation (CPLEX [2], logical constraints) (3) Placement of BIST Logic for each Partition ([3], Min-Weight Max-Cardinality Matching) Output: Memory BIST Groups, Test Schedules, BIST Logic Placements [1] MLPart. http://vlsicad.ucsd.edu/GSRC/bookshelf/Slots/Partitioning/MLPart [2] IBM ILOG CPLEX Optimizer. http://www.ilog.com/products/cplex [3] Hungarian algorithm source code. http://www.informatik.uni-freiburg.de/~stachnis/index.html

Memory Partitioning Divide memories into k partitions using MLPart MLPart: Min-cut hypergraph partitioner based on multilevel FM k = number of BIST controllers Hypergraph: vertices = memories; hyperedge weights reflect test time implications of grouping Reduced test time if memories with same shape and depth in same group MEM Hyperedges: The same shape, depth and test power, respectively Edges: Distances < D1, < avg(D1, D2) and < D2, respectively (where D1 < D2) Example Hypergraph

Test Scheduling Solve ILP formulation Minimize test time, subject to power constraint ILP solver: CPLEX Optimizer 12.5.1 Additional partition (BIST controller) for better test time Obtain alternative test scheduling solution with extra BIST controller {1 BIST controller, 2 BIST controllers} per each partition MEM BIST1 Est. Test Time 1000 MEM BIST1 BIST2 Est. Test Time 900 Solution with one BIST controller Solution with two BIST controllers

Memory BIST Logic Placement To minimize wirelength between each BIST and memories Min-weight maximum-cardinality matching in a bipartite graph Apply Hungarian algorithm Cost: Diameter from a grid to all memories in a group Forbidden grids (intersection with memories) MEM MEM MEM MEM MEM MEM MEM MEM BIST placement solution MEM Possible grids for BIST logic placement Memories in the same BIST group

Outline Motivation and Contributions Related Works ILP Formulation Heuristic Flow Experimental Results Conclusions and Future Work

Experimental Setup Develop MBIST-solver to implement our heuristics C++ / g++ 4.8.0 User options include min/max # partitions, min/max # memories in a partition, max diameter, power constraint, weight parameters, … MLPart: Memory partitioner CPLEX 12.5.1: ILP solver Testcases: six industrial testcases (from industry partners) TC #M #P DMAX TC1 143 13 3900 TC2 150 11 4500 TC3 124 8 2200 TC4 160 3400 TC5 137 7 3200 TC6 148 12 4100 TC = testcase M = #memories P = #partitions DMAX = maximum diameter w/o BIST [μm]

Industrial solution (TC1) Experimental Results Our solutions Reduce test time by up to 11.57% (Testcase 3) Reduce number of BIST controllers by up to 41.6% (Testcase 6) Reduce maximum diameter by up to 31.7% (Testcase 6) Large Maximum Diameter Smaller Maximum Diameter memories BIST controller Industrial solution (TC1) Test time = 1067 # BISTs = 13 Maximum Diameter = 3900μm Our solution (TC1) Test time = 969 # BISTs = 12 Maximum Diameter = 2100μm

Outline Motivation and Contributions Related Works ILP Formulation Heuristic Flow Experimental Results Conclusions and Future Work

Conclusions and Future Work Three-step heuristic methodology Co-optimization of (1) Memory BIST grouping, (2) test scheduling, and (3) memory BIST logic placement Objective: minimize test time for a given #BIST controllers Handle various serial / parallel testing options in ILP Minimize diameter of BIST groups  less-critical timing, less need for LVT instances in BIST implementation, etc. Future work Integrate these optimizations into ‘production’ PD flow Improve hypergraph weighting, scalability of ILP Improve congestion- and timing-awareness of BIST placement

Thank You!