skip to main content
research-article

ROC: A Reconfigurable Optical Computer for Simulating Physical Processes

Published: 09 March 2020 Publication History

Abstract

Due to the end of Moore’s law and Dennard scaling, we are entering a new era of processors. Computing systems are increasingly facing power and performance challenges due to both device- and circuit-related challenges with resistive and capacitive charging. Non-von Neumann architectures are needed to support future computations through innovative post-Moore’s law architectures. To enable these emerging architectures with high-performance and at ultra-low power, both parallel computation and inter-node communication on-the-chip can be supported using photons. To this end, we introduce ROC, a reconfigurable optical computer that can solve partial differential equations (PDEs). PDE solvers form the basis for many traditional simulation problems in science and engineering that are currently performed on supercomputers. Instead of solving problems iteratively, the proposed engine uses a resistive mesh architecture to solve a PDE in a single iteration (one-shot). Instead of using actual electrical circuits, the physical underlying hardware emulates such structures using a silicon-photonics mesh that splits light into separate pathways, allowing it to add or subtract optical power analogous to programmable resistors. The time to obtain the PDE solution then only depends on the time-of-flight of a photon through the programmed mesh, which can be on the order of 10’s of picoseconds given the millimeter-compact integrated photonic circuit. Numerically validated experimental results show that, over multiple configurations, ROC can achieve several orders of magnitude improvement over state-of-the-art GPUs when speed, power, and size are taken into account. Further, it comes within approximately 90% precision of current numerical solvers. As such, ROC can be a viable reconfigurable, approximate computer with the potential for more precise results when replacing silicon-photonics building blocks with nanoscale photonic lumped-elements.

References

[1]
Defense Advanced Research Project Agency. 2017. Electronics Resurgence Initiative: Page 3 Investments Architectures Thrust. Retrieved on January 20, 2018 from https://www.darpa.mil/news-events/2017-09-13.
[2]
J. Anderson, S. Sun, Y. Alkabani, V. Sorger, and T. El-Ghazawi. 2019. Photonic processor for fully discretized neural networks. In Proceedings of the IEEE International Conference on Application-Specific Systems, Architectures, and Processors (ASAP’19).
[3]
Y. Li, Yue, I. Liberal, C. D. Giovampaola, and N. Engheta. 2016. Waveguide metatronics: Lumped circuitry based on structural dispersion. Sci. Adv. 2, 6 (2016).
[4]
K. Papadimitriou, A. Anyfantis, and A. Dollas. 2007. Methodology and experimental setup for the determination of system-level dynamic reconfiguration overhead. In Proceedings of the IEEE International Symposium on Field-Programmable Custom Computing Machines (FCCM’07).
[5]
K. Papadimitriou, A. Dollas, and S. Hauck. 2011. Performance of partial reconfiguration in FPGA systems: A survey and a cost model. ACM Trans. Reconfigur. Technol. Syst. 4, 4 (2011).
[6]
K. Papadimitriou, A. Anyfantis, and A. Dollas. 2010. An effective framework to evaluate dynamic partial reconfiguration in FPGA systems. IEEE Trans. Instrument. Measure. 59, 6 (2010).
[7]
A. Agrawal, J. Choi, K. Gopalakrishnan, S. Gupta, R. Nair, J. Oh, D. Prener, S. Shukla, V. Srinivasan, and Z. Sura. 2016. Approximate computing: Challenges and opportunities. In Proceedings of the IEEE International Conference on Rebooting Computing (ICRC’16).
[8]
T. El-Ghazawi, V. J. Sorger, S. Sun, A. A. Badawy, and V. Narayana. 2019. Reconfigurable optical computer (June 2019). Patent No. U.S. 10,318,680 B2. Filed December 5, 2016, issued June 11, 2019.
[9]
S. Mittal. 2016. Survey of techniques for approximate computing. In ACM Computing Surveys. Association of Computing Machinists.
[10]
S. Farlow. 1993. Partial Differential Equations for Scientists and Engineers. Dover.
[11]
J. Zhu. 1994. Solving Partial Differential Equations on Parallel Computers. World Scientific, River Edge, NJ.
[12]
L. Pinuel, I. Martin, and F. Tirado. 1998. A special-purpose parallel computer for solving partial differential equations. In Proceedings of the 6th Euromicro Workshop on Parallel and Distributed Processing. IEEE.
[13]
G. Liebmann. 1950. Solution of Partial Differential Equations with a Resistance Network Analogue. British Journal of Applied Physics, Institute of Physics.
[14]
P. Palmer, A. R. Copson, and S. C. Redshaw. 1959. Investigations into the Use of an Electrical Resistance Analogue for the Solution of Certain Oscillatory-Flow Problems, Reports and Memoranda No. 312. Aeronautical Research Council.
[15]
J. Ramirez-Angulo and Mark R. DeYong. 2000. Digitally-configurable analog VLSI Chip and Method for Real-time Solution of Partial Differential Equations, U.S. Patent US6141676 A.
[16]
A. Hastings. 2005. The Art of Analog Layout, 2nd ed. Prentice Hall.
[17]
G. Liebmann. 1954. Resistance-network Analogues with Unequal Meshes or Subdivided Meshes. British Journal of Applied Physics, Institute of Physics.
[18]
V. K. Narayana, O. Serres, J. Lau, S. Licht, and T. El-Ghazawi. 2014. Towards a computational model for heat transfer in electrolytic cells. Int. J. Comput. Theory Eng. 6, 3 (2014).
[19]
W. Schiesser. 1991. The Numerical Method of Lines, 1st Edition, Integration of Partial Differential Equations. Elsevier.
[20]
R. Wienands and W. Joppich. 2005. Practical Fourier Analysis for Multigrid Methods. CRC Press.
[21]
H. J. Lee and W. E. Schiesser. 2003. Ordinary and Partial Differential Equation Routines in C, C++, Fortran, Java, Maple and MATLAB. CRC Press.
[22]
R. M. M. Mattheij, S. W. Rienstra, and J. H. M. ten Thije Boonkkamp. 2005. Partial differential equations: Modeling, analysis, computation. Soc. Industr. Appl. Math. (2005), 79--82.
[23]
R. M. Corless. 2002. A new view of the computational complexity of IVP for ODE. Numer. Algor. 31, 1 (2002), 115--124.
[24]
P. Chi, S. Li, C. Xu, T. Zhang, J. Zhao, Y. Liu, Y. Wang, and Y. Xie. 2016. PRIME: A novel processing-in-memory architecture for neural network computation in ReRAM-based main memory. In Proceedings of ACM/IEEE 43rd Annual International Symposium on Computer Architecture.
[25]
M. Hu, J. P. Strachan, Z. Li, E. M. Grafals, N. Davila, C. Graves, S. Lam, N. Ge, J. J. Yang, and R. S. Williams. 2016. Dot-Product Engine for Neuromorphic Computing: Programming 1T1M Crossbar to Accelerate Matrix-Vector Multiplication, HPE-2016-23, Hewlett-Packard Labs.
[26]
Texas Instruments. 2016. OPA857 Ultralow-Noise, Wideband, Selectable-Feedback Resistance Transimpedance Amplifier, Revision D.
[27]
Texas Instruments. 2005. TS5N118 1-OF-8 FET Multiplexer/Demultiplexer High-bandWidth Bus Switch.
[28]
Texas Instruments. 2007. ADS1605 16-Bit, 5MSPS Analog-to-Digital Converter.
[29]
I. Herron and M. Foster. 2008. Partial Differential Equations in Fluid Dynamics. Cambridge Press.
[30]
M. N. O. Sadiku and L. C. Agba. 1990. A simple introduction to the transmission-line modeling. IEEE Trans. Circ. Syst. 37, 8 (1990), IEEE.
[31]
H. Berestycki and Y. Pomeau (eds). 2002. Nonlinear PDE’s in Condensed Matter and Reactive Flows. Springer.
[32]
A. P. S. Selvadurai. 2000. Partial Differential Equations in Mechanics 1: Fundamentals, Laplace’s Equation, Diffusion Equation, Wave Equation. Springer.
[33]
C. Tang, Q. Mi, H. Yan, J. Yang, and S. Liu. 2013. PDE (ODE)-based image processing methods for optical interferometry fringe. In Proceedings of International Conference on Optics in Precision Engineering and Nanotechnology.
[34]
C. D. Sarris. 2006. Adaptive mesh refinement in time-domain numerical electromagnetics. Synthesis Lectures on Computational Electromagnetics. Morgan 8 Claypool.
[35]
M. J. Berger and J. Oliger. 1984. Adaptive mesh refinement for hyperbolic partial differential equations. J. Comput. Phys. 53, 3 (1984), Elsevier.
[36]
G. Liebmann. 1955. An Improved Experimental Iteration Method for Use with Resistance-network Analogues. British Journal of Applied Physics, Institute of Physics.
[37]
M. N. Bojnordi and E. Ipek. 2016. Memristive Boltzmann machine: A hardware accelerator for combinatorial optimization and deep learning. In Proceedings of the IEEE International Symposium on High Performance Computer Architecture. IEEE.
[38]
L. C. Evans. 1998. Partial Differential Equations. American Mathematical Society, Providence, RI.
[39]
A. Benoit, F. Chyzak, A. Darrasse, S. Gerhold, M. Mezzarobba, and B. Salvy. 2010. The Dynamic Dictionary of Mathematical Functions. International Congress on Mathematical Software, Springer, Berlin.
[40]
M. Alioto. 2017. Energy-quality scalable adaptive VLSI circuits and systems beyond approximate computing. In Proceedings of the Design, Automation 8 Test in Europe Conference 8 Exhibition. IEEE.
[41]
J. George, H. Nejadriahi, and V. J. Sorger. 2017. Towards On-Chip Optical FFTs for Convolutional Neural Networks. In Proceedings of the IEEE International Conference on Rebooting Computing (ICRC’17). IEEE.
[42]
J. Dongarra. 2017. Current Trends in High Performance Computing and Challenges for the Future. ACM Learning Webinar.
[43]
V. J. Sorger, N. D. Lanzillotti-Kimura, R. M. Ma, and X. Zhang. 2012. Ultra-compact silicon nanophotonic modulator with broadband response. Nanophotonics 1, 1 (2012).
[44]
S. Sun, V. K. Narayana, I. Sarpkaya, J. Crandall, R. A. Soref, H. Dalir, T. El-Ghazawi, and V. J. Sorger. 2017. Hybrid photonic-plasmonic non-blocking broadband 5x5 router for optical networks. IEEE Photon. J. 10, 2 (2017), 1--13.
[45]
H. Yin. 2011. Application of Resistivity-Tool-Response Modeling For Formation Evaluation: AAPG Archive Series 2, 2. AAPG.
[46]
P. B. Hansen. 1992. Numerical Solution of Laplace’s Equation, Electrical Engineering and Computer Science Technical Reports, 9-1992, Syracuse University.
[47]
J. D. Irwin and R. M. Nelms. 2015. Basic Engineering Circuit Analysis, 11th ed. Wiley.
[48]
T. Meis and U. Marcowitz. 1981. Numerical Solution of Partial Differential Equations. Springer-Verlag.
[49]
A. R. Sanderson, M. D. Meyer, R. M. Kirby, and C. R. Johnson. 2009. A framework for exploring numerical solutions of advection-reaction-diffusion equations using a GPU-based approach. Comput. Visual. Sci. 12, 4 (2009).
[50]
Y. Zhao. 2008. Lattice Boltzmann-based PDE solver on the GPU. Visual Comput. 24, 5 (2008).
[51]
M. Karunaratne, A. K. Mohite, T. Mitra, and L. S. Peh. 2017. HyCUBE: A CGRA with reconfigurable single-cycle multi-hop interconnect. In Proceedings of the 54th Annual Design Automation Conference (DAC’17). ACM.
[52]
Xilinx Corporation. 2008. XST User Guide, v. 10.1 (2008).
[53]
J. Jeffers, J. Reinders, and A. Sodani. 2016. Intel Xeon Phi Processor High Performance Programming: Knights Landing Edition, 2nd ed. Morgan Kaufmann.
[54]
M. T. Ibn Ziad, M. Hossam, M. A. Masoud, M. Nagy, H. A. Adel, Y. Alkabani, M. W. El-Kharashi, K. Salah, and M. AbdelSalamb. 2015. On Kernel Acceleration of Electromagnetic Solvers via Hardware Emulation. Elsevier.
[55]
R. Ji, J. Xu, and L. Yang. 2013. Five-port optical router based on microring switches for photonic networks-on-chip. In IEEE Photonics Technology Letters. IEEE.
[56]
Y. Jiao, S. F. Mingaleev, M. Schillinger, D. Miller, S. Fan, and K. Busch. 2005. Wannier basis design and optimization of a photonic crystal waveguide crossing. IEEE Photonics Technology Letters. IEEE.
[57]
P. Bastian, K. Birken, K. Johannsen, S. Lang, N. Neu Rentz-Reichert, and C. Wieners. 1997. UG—A flexible software toolbox for solving partial differential equations. Comput. Visual. Sci. 1, 1 (1997).
[58]
X. Li. 2005. An overview of SuperLU: Algorithms, implementation, and user interface. ACM Trans. Math. Softw. 31, 3 (2005).
[59]
N. Sherwood-Droz, H. Wang, L. Chen, B. G. Lee, A. Biberman, K. Bergman, and M. Lipson. 2008. Optical 4×4 hitless silicon router for optical Networks-on-Chip (NoC). Optics Expr. 16, 20 (2008).
[60]
H. K. Tsang and Y. Liu. 2008. Nonlinear optical properties of silicon waveguides. Semicond. Sci. Technol. 23, 6 (2008), 064007.
[61]
N. Engheta. 2007. Circuits with light at nanoscales: Optical nanocircuits inspired by metamaterials. Science 317, 5845 (2007), 1698--1702.
[62]
A. Silva, F. Monticone, G. Castaldi, V. Galdi, A. Alu, and N. Engheta. 2014. Performing mathematical operations with metamaterials. Science 343, 6167 (2014), 160--163.
[63]
A. Mehrabian, Y. Alkabani, V. Sorger, and T. El-Ghazawi. 2018. PCNNA: A photonic convolutional neural network accelerator. arXiv:1807.08792.
[64]
Institute of Electrical and Electronics Engineers. 2013. >1149.1-2013—IEEE Standard for Test Access Port and Boundary-Scan Architecture. IEEE.
[65]
J. Townsend. 2012. A Modern Approach to Quantum Mechanics, 2nd ed. University Science Books.
[66]
AIM Photonics. 2019. Retrieved from http://www.aimphotonics.com.

Cited By

View all
  • (2025)Photonic NP-Complete Problem Solver Enabled by Local Spatial Frequency EncodingACS Photonics10.1021/acsphotonics.4c01795Online publication date: 20-Feb-2025
  • (2022)Towards All-optical Stochastic Computing Using Photonic Crystal NanocavitiesACM Journal on Emerging Technologies in Computing Systems10.1145/348487118:1(1-25)Online publication date: 31-Jan-2022
  • (2021)Quantifying Information via Shannon Entropy in Spatially Structured Optical BeamsResearch10.34133/2021/97807602021Online publication date: Jan-2021
  • Show More Cited By

Recommendations

Comments

Information & Contributors

Information

Published In

cover image ACM Transactions on Parallel Computing
ACM Transactions on Parallel Computing  Volume 7, Issue 1
Special Issue on Innovations in Systems for Irregular Applications, Part 1 and Regular Paper
March 2020
182 pages
ISSN:2329-4949
EISSN:2329-4957
DOI:10.1145/3387354
Issue’s Table of Contents
Permission to make digital or hard copies of all or part of this work for personal or classroom use is granted without fee provided that copies are not made or distributed for profit or commercial advantage and that copies bear this notice and the full citation on the first page. Copyrights for components of this work owned by others than ACM must be honored. Abstracting with credit is permitted. To copy otherwise, or republish, to post on servers or to redistribute to lists, requires prior specific permission and/or a fee. Request permissions from [email protected]

Publisher

Association for Computing Machinery

New York, NY, United States

Publication History

Published: 09 March 2020
Accepted: 01 October 2019
Revised: 01 August 2019
Received: 01 December 2018
Published in TOPC Volume 7, Issue 1

Permissions

Request permissions for this article.

Check for updates

Author Tags

  1. PDE solver accelerator
  2. analog computing
  3. approximate computing
  4. nanophotonic computing
  5. optical computing
  6. post-Moore’s law processors

Qualifiers

  • Research-article
  • Research
  • Refereed

Funding Sources

  • CSR-Computer Systems Research
  • NSF EPMD-ElectroPhotonic Mag Devices
  • Networking Technology and Systems
  • NSF RAISE program

Contributors

Other Metrics

Bibliometrics & Citations

Bibliometrics

Article Metrics

  • Downloads (Last 12 months)28
  • Downloads (Last 6 weeks)4
Reflects downloads up to 01 Mar 2025

Other Metrics

Citations

Cited By

View all
  • (2025)Photonic NP-Complete Problem Solver Enabled by Local Spatial Frequency EncodingACS Photonics10.1021/acsphotonics.4c01795Online publication date: 20-Feb-2025
  • (2022)Towards All-optical Stochastic Computing Using Photonic Crystal NanocavitiesACM Journal on Emerging Technologies in Computing Systems10.1145/348487118:1(1-25)Online publication date: 31-Jan-2022
  • (2021)Quantifying Information via Shannon Entropy in Spatially Structured Optical BeamsResearch10.34133/2021/97807602021Online publication date: Jan-2021
  • (2021)Induced homomorphism: Kirchhoff’s law in photonicsNanophotonics10.1515/nanoph-2020-0655Online publication date: 22-Mar-2021
  • (2021)Reconfigurable plasma-dielectric hybrid photonic crystal as a platform for electromagnetic wave manipulation and computingPhysics of Plasmas10.1063/5.004333628:4(043502)Online publication date: Apr-2021
  • (2020)Software stack for an analog mesh computerProceedings of the 17th ACM International Conference on Computing Frontiers10.1145/3387902.3394030(241-244)Online publication date: 11-May-2020
  • (2020)Virtualizing Analog Mesh Computers: The Case of a Photonic PDE Solving Accelerator2020 International Conference on Rebooting Computing (ICRC)10.1109/ICRC2020.2020.00008(133-142)Online publication date: Dec-2020
  • (2020)A Design Methodology for Post-Moore’s Law Accelerators: The Case of a Photonic Neuromorphic Processor2020 IEEE 31st International Conference on Application-specific Systems, Architectures and Processors (ASAP)10.1109/ASAP49362.2020.00028(113-116)Online publication date: Jul-2020

View Options

Login options

Full Access

View options

PDF

View or Download as a PDF file.

PDF

eReader

View online with eReader.

eReader

HTML Format

View this article in HTML Format.

HTML Format

Figures

Tables

Media

Share

Share

Share this Publication link

Share on social media