3DGates: An Instruction-Level Energy Analysis and Optimization of 3D Printers

As the next-generation manufacturing driven force, 3D printing technology is having a transformative effect on various industrial domains and has been widely applied in a broad spectrum of applications. It also progresses towards other versatile fields with portable battery-powered 3D printers working on a limited energy budget. While reducing manufacturing energy is an essential challenge in industrial sustainability and national economics, this growing trend motivates us to explore the energy consumption of the 3D printer for the purpose of energy efficiency. To this end, we perform an in-depth analysis of energy consumption in commercial, off-the-shelf 3D printers from an instruction-level perspective. We build an instruction-level energy model and an energy profiler to analyze the energy cost during the fabrication process. From the insights obtained by the energy profiler, we propose and implement a cross-layer energy optimization solution, called 3DGates, which spans the instruction-set, the compiler and the firmware. We evaluate 3DGates over 338 benchmarks on a 3D printer and achieve an overall energy reduction of 25%.

[1]  D. Frear,et al.  Intermetallic growth and mechanical behavior of low and high melting temperature solder alloys , 1994 .

[2]  Mike Paterson,et al.  Longest Common Subsequences , 1994, MFCS.

[3]  Matthias Dipl Ing Greul,et al.  Fast, functional prototypes via multiphase jet solidification , 1995 .

[4]  Jacek F. Gieras,et al.  Permanent magnet motor technology : design and applications , 1996 .

[5]  Sharad Malik,et al.  Instruction level power analysis and optimization of software , 1996, Proceedings of 9th International Conference on VLSI Design.

[6]  Gurindar S. Sohi,et al.  A static power model for architects , 2000, MICRO 33.

[7]  C. D. Hazen Watts up? , 2000, Health facilities management.

[8]  M. Zimmerman,et al.  Three-dimensional printing and porous metallic surfaces: a new orthopedic application. , 2001, Journal of biomedical materials research.

[9]  Margaret Martonosi,et al.  Run-time power estimation in high performance microprocessors , 2001, ISLPED '01.

[10]  Leszek Hozer,et al.  Fabrication of functionally graded reaction infiltrated SiC–Si composite by three-dimensional printing (3DP™) process , 2001 .

[11]  W. T. Lei,et al.  Accuracy test of five-axis CNC machine tool with 3D probe-ball. Part I: Design and modeling , 2002 .

[12]  W. T. Lei,et al.  Accuracy test of five-axis CNC machine tool with 3D probe-ball. Part II: errors estimation , 2002 .

[13]  D. Hutmacher,et al.  Scaffold development using 3D printing with a starch-based polymer , 2002 .

[14]  Qi Yang,et al.  Energy-aware partitioning for multiprocessor real-time systems , 2003, Proceedings International Parallel and Distributed Processing Symposium.

[15]  Vijay Janapa Reddi,et al.  PIN: a binary instrumentation tool for computer architecture research and education , 2004, WCAE '04.

[16]  Peter I. Cowling,et al.  MMAC: a new multi-class, multi-label associative classification approach , 2004, Fourth IEEE International Conference on Data Mining (ICDM'04).

[17]  Jason Flinn,et al.  Ghosts in the machine: interfaces for better power management , 2004, MobiSys '04.

[18]  Gilberto Contreras,et al.  Power prediction for Intel XScale processors using performance monitoring unit events , 2005 .

[19]  Stephane M. Morvan,et al.  Steel Parts with Tailored Material Gradients by 3D-Printing Using Nano-Particulate Ink , 2005 .

[20]  Srdjan M. Lukic,et al.  Topological overview of hybrid electric and fuel cell vehicular power system architectures and configurations , 2005, IEEE Transactions on Vehicular Technology.

[21]  Margaret Martonosi,et al.  Power prediction for Intel XScale/spl reg/ processors using performance monitoring unit events , 2005, ISLPED '05. Proceedings of the 2005 International Symposium on Low Power Electronics and Design, 2005..

[22]  Jintamai Suwanprateeb,et al.  Improvement in mechanical properties of three‐dimensional printing parts made from natural polymers reinforced by acrylate resin for biomedical applications: a double infiltration approach , 2006 .

[23]  Nicholas Nethercote,et al.  Valgrind: a framework for heavyweight dynamic binary instrumentation , 2007, PLDI '07.

[24]  Chenyang Lu,et al.  Integrating concurrency control and energy management in device drivers , 2007, SOSP.

[25]  Meeta Sharma Gupta,et al.  System level analysis of fast, per-core DVFS using on-chip switching regulators , 2008, 2008 IEEE 14th International Symposium on High Performance Computer Architecture.

[26]  Sandeep K. S. Gupta,et al.  Energy-Efficient Thermal-Aware Task Scheduling for Homogeneous High-Performance Computing Data Centers: A Cyber-Physical Approach , 2008, IEEE Transactions on Parallel and Distributed Systems.

[27]  S. R. Devadasan,et al.  Agility through rapid prototyping technology in a manufacturing environment using a 3D printer , 2009 .

[28]  Xi He,et al.  Power-aware scheduling of virtual machines in DVFS-enabled clusters , 2009, 2009 IEEE International Conference on Cluster Computing and Workshops.

[29]  Heng Tao Shen,et al.  Principal Component Analysis , 2009, Encyclopedia of Biometrics.

[30]  Gernot Heiser,et al.  An Analysis of Power Consumption in a Smartphone , 2010, USENIX Annual Technical Conference.

[31]  Timothy Mattson,et al.  A 48-Core IA-32 message-passing processor with DVFS in 45nm CMOS , 2010, 2010 IEEE International Solid-State Circuits Conference - (ISSCC).

[32]  Lei Yang,et al.  Accurate online power estimation and automatic battery behavior based power model generation for smartphones , 2010, 2010 IEEE/ACM/IFIP International Conference on Hardware/Software Codesign and System Synthesis (CODES+ISSS).

[33]  Costas P. Grigoropoulos,et al.  Metal nanoparticle direct inkjet printing for low-temperature 3D micro metal structure fabrication , 2010 .

[34]  Gregor von Laszewski,et al.  Towards Energy Aware Scheduling for Precedence Constrained Parallel Tasks in a Cluster with DVFS , 2010, 2010 10th IEEE/ACM International Conference on Cluster, Cloud and Grid Computing.

[35]  Lihui Wang,et al.  Adaptive tool-path generation of rapid prototyping for complex product models , 2011 .

[36]  Henry Hoffmann,et al.  Dynamic knobs for responsive power-aware computing , 2011, ASPLOS XVI.

[37]  Igor Drstvenšek,et al.  Speed and accuracy evaluation of additive manufacturing machines , 2011 .

[38]  Karl-Erik Årzén,et al.  Resource Management on Multicore Systems: The ACTORS Approach , 2011, IEEE Micro.

[39]  Paramvir Bahl,et al.  Fine-grained power modeling for smartphones using system call tracing , 2011, EuroSys '11.

[40]  Howie Choset,et al.  Design and architecture of the unified modular snake robot , 2012, 2012 IEEE International Conference on Robotics and Automation.

[41]  Ming Zhang,et al.  Where is the energy spent inside my app?: fine grained energy accounting on smartphones with Eprof , 2012, EuroSys '12.

[42]  Richard M. Everson,et al.  A new approach to the design and optimisation of support structures in additive manufacturing , 2013 .

[43]  Neri Volpato,et al.  Reducing repositioning distances in fused deposition-based processes using optimization algorithms , 2013 .

[44]  John Crossley,et al.  A sub-ns response fully integrated battery-connected switched-capacitor voltage regulator delivering 0.19W/mm2 at 73% efficiency , 2013, 2013 IEEE International Solid-State Circuits Conference Digest of Technical Papers.

[45]  Scott E. Hudson,et al.  Printing teddy bears: a technique for 3D printing of soft interactive objects , 2014, CHI.

[46]  Gokula Vijayumar Annamalai Vasantha,et al.  RELATIVE ENERGY CONSUMPTION OF LOW-COST 3D PRINTERS , 2014 .

[47]  Chao Xu,et al.  Automated OS-level Device Runtime Power Management , 2015, ASPLOS.

[48]  Tao Peng,et al.  Analysis of Energy Utilization in 3D Printing Processes , 2016 .

[49]  Ramgopal R. Mettu,et al.  Beyond layers: A 3D-aware toolpath algorithm for fused filament fabrication , 2016, 2016 IEEE International Conference on Robotics and Automation (ICRA).

[50]  Terry Wohlers,et al.  Wohlers report 2016 , 2016 .

[51]  Wolfgang Wohlers Zur (Un-)Verwertbarkeit strafrechtswidrig erhobener Bild- und Audioaufzeichnungen des Tatgeschehens , 2016 .

[52]  Aditya Singh Rathore,et al.  Don't Forget Your Electricity Bills!: An Empirical Study of Characterizing Energy Consumption of 3D Printers , 2016, APSys.