Introduction to approximate computing: Embedded tutorial

A new design paradigm - approximate computing - was established to investigate how computer systems can be made better - more energy efficient, faster, and less complex - by relaxing the requirement that they are exactly correct. The purpose of this paper is to introduce the principles of approximate computing and survey the research conducted in major subareas of approximate computing which are relevant for design and test of digital circuits.

[1]  Nikil Dutt,et al.  Partially-Forgetful Memories : Relaxing Memory Guard-bands for Approximate Computing , 2015 .

[2]  Ing-Chao Lin,et al.  High accuracy approximate multiplier with error correction , 2013, 2013 IEEE 31st International Conference on Computer Design (ICCD).

[3]  Kaushik Roy,et al.  Approximate computing: An integrated hardware approach , 2013, 2013 Asilomar Conference on Signals, Systems and Computers.

[4]  Kaushik Roy,et al.  SALSA: Systematic logic synthesis of approximate circuits , 2012, DAC Design Automation Conference 2012.

[5]  Rolf Drechsler,et al.  BDD minimization for approximate computing , 2016, 2016 21st Asia and South Pacific Design Automation Conference (ASP-DAC).

[6]  Andrew P. Black,et al.  Proceedings of the 2014 ACM International Conference on Object Oriented Programming Systems Languages & Applications , 2014, OOPSLA.

[7]  Sherief Reda,et al.  ABACUS: A technique for automated behavioral synthesis of approximate computing circuits , 2014, 2014 Design, Automation & Test in Europe Conference & Exhibition (DATE).

[8]  Kaushik Roy,et al.  Analysis and characterization of inherent application resilience for approximate computing , 2013, 2013 50th ACM/EDAC/IEEE Design Automation Conference (DAC).

[9]  Anand Raghunathan,et al.  Relax-and-Retime: A methodology for energy-efficient recovery based design , 2013, 2013 50th ACM/EDAC/IEEE Design Automation Conference (DAC).

[10]  Lukás Sekanina,et al.  Evolutionary Approach to Approximate Digital Circuits Design , 2015, IEEE Transactions on Evolutionary Computation.

[11]  Lukás Sekanina,et al.  Evolutionary design of complex approximate combinational circuits , 2015, Genetic Programming and Evolvable Machines.

[12]  Puneet Gupta,et al.  Trading Accuracy for Power in a Multiplier Architecture , 2011, J. Low Power Electron..

[13]  Kaushik Roy,et al.  Multiplier-less Artificial Neurons exploiting error resiliency for energy-efficient neural computing , 2016, 2016 Design, Automation & Test in Europe Conference & Exhibition (DATE).

[14]  Andreas Peter Burg,et al.  Mitigating the impact of faults in unreliable memories for error-resilient applications , 2015, 2015 52nd ACM/EDAC/IEEE Design Automation Conference (DAC).

[15]  Wei Luo,et al.  Joint precision optimization and high level synthesis for approximate computing , 2015, 2015 52nd ACM/EDAC/IEEE Design Automation Conference (DAC).

[16]  Kartik Mohanram,et al.  Low Cost Concurrent Error Masking Using Approximate Logic Circuits , 2013, IEEE Transactions on Computer-Aided Design of Integrated Circuits and Systems.

[17]  John Augustine,et al.  Opportunities for energy efficient computing: A study of inexact general purpose processors for high-performance and big-data applications , 2015, 2015 Design, Automation & Test in Europe Conference & Exhibition (DATE).

[18]  Dan Grossman,et al.  EnerJ: approximate data types for safe and general low-power computation , 2011, PLDI '11.

[19]  Kaushik Roy,et al.  Low-Power Digital Signal Processing Using Approximate Adders , 2013, IEEE Transactions on Computer-Aided Design of Integrated Circuits and Systems.

[20]  Jie Han,et al.  Approximate computing: An emerging paradigm for energy-efficient design , 2013, 2013 18th IEEE European Test Symposium (ETS).

[21]  Kaushik Roy,et al.  ASLAN: Synthesis of approximate sequential circuits , 2014, 2014 Design, Automation & Test in Europe Conference & Exhibition (DATE).

[22]  Sparsh Mittal,et al.  A Survey of Techniques for Approximate Computing , 2016, ACM Comput. Surv..

[23]  Chundong Wang,et al.  ASAC: automatic sensitivity analysis for approximate computing , 2014, LCTES '14.

[24]  John Sartori,et al.  Slack redistribution for graceful degradation under voltage overscaling , 2010, 2010 15th Asia and South Pacific Design Automation Conference (ASP-DAC).

[25]  Anand Raghunathan,et al.  Approximation through logic isolation for the design of quality configurable circuits , 2016, 2016 Design, Automation & Test in Europe Conference & Exhibition (DATE).

[26]  Kaushik Roy,et al.  MACACO: Modeling and analysis of circuits for approximate computing , 2011, 2011 IEEE/ACM International Conference on Computer-Aided Design (ICCAD).

[27]  Martin C. Rinard,et al.  Chisel: reliability- and accuracy-aware optimization of approximate computational kernels , 2014, OOPSLA.

[28]  Deming Chen,et al.  CCP: common case promotion for improved timing error resilience with energy efficiency , 2012, ISLPED '12.

[29]  Shih-Lien Lu.  Speeding Up Processing with Approximation Circuits , 2004, Computer.

[30]  Lara Dolecek,et al.  Underdesigned and Opportunistic Computing in Presence of Hardware Variability , 2013, IEEE Transactions on Computer-Aided Design of Integrated Circuits and Systems.

[31]  Constantine Bekas,et al.  An extreme-scale implicit solver for complex PDEs: highly heterogeneous flow in earth's mantle , 2015, SC15: International Conference for High Performance Computing, Networking, Storage and Analysis.

[32]  Kaushik Roy,et al.  Substitute-and-simplify: A unified design paradigm for approximate and quality configurable circuits , 2013, 2013 Design, Automation & Test in Europe Conference & Exhibition (DATE).

[33]  Kaushik Roy,et al.  Quality programmable vector processors for approximate computing , 2013, 2013 46th Annual IEEE/ACM International Symposium on Microarchitecture (MICRO).

[34]  Ehsanollah Kabir,et al.  Approximate Arithmetic for Low-Power Image Median Filtering , 2015, Circuits Syst. Signal Process..

[35]  Youtao Zhang,et al.  Proceedings of the 2014 SIGPLAN/SIGBED conference on Languages, compilers and tools for embedded systems , 2014, LCTES 2014.

[36]  Jacob Nelson,et al.  Approximate storage in solid-state memories , 2013, MICRO-46.

[37]  Lukás Sekanina,et al.  Circuit Approximation Using Single- and Multi-objective Cartesian GP , 2015, EuroGP.

[38]  Kia Bazargan,et al.  Axilog: Language support for approximate hardware design , 2015, 2015 Design, Automation & Test in Europe Conference & Exhibition (DATE).

[39]  Luis Ceze,et al.  Neural Acceleration for General-Purpose Approximate Programs , 2014, IEEE Micro.