Exploiting Approximate Computing for Low-Cost Fault Tolerant Architectures

This work investigates how the approximate computing paradigm can be exploited to provide low-cost fault tolerant architectures. In particular, we focus on the implementation of Approximate Triple Modular Redundancy (ATMR) designs using the precision reduction technique. The proposed method is applied to two benchmarks and a multitude of ATMR designs with different degrees of approximation. The benchmarks are implemented on a Xilinx Zynq-7000 APSoC FPGA through high-level synthesis and evaluated concerning area usage and the inaccuracy caused by approximation. Fault injection experiments are performed by flipping bits of the FPGA configuration bitstream. Results show that the proposed approximation method can decrease the DSP usage of the hardware implementation up to 80% and the number of sensitive configuration bits up to 75% while maintaining an accuracy of more than 99.96%.

[1]  Fernanda Gusmão de Lima Kastensmidt,et al.  Method to Analyze the Susceptibility of HLS Designs in SRAM-Based FPGAs Under Soft Errors , 2016, ARC.

[2]  Mayler G. A. Martins,et al.  Exploring the use of approximate TMR to mask transient faults in logic with low area overhead , 2015, Microelectron. Reliab..

[3]  Jeong-A Lee,et al.  Probing Approximate TMR in Error Resilient Applications for Better Design Tradeoffs , 2016, 2016 Euromicro Conference on Digital System Design (DSD).

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

[5]  Henry Hoffmann,et al.  Managing performance vs. accuracy trade-offs with loop perforation , 2011, ESEC/FSE '11.

[6]  L. Entrena,et al.  Partial TMR in FPGAs Using Approximate Logic Circuits , 2015, 2015 15th European Conference on Radiation and Its Effects on Components and Systems (RADECS).

[7]  James Demmel,et al.  Precimonious: Tuning assistant for floating-point precision , 2013, 2013 SC - International Conference for High Performance Computing, Networking, Storage and Analysis (SC).

[8]  Tanya Vladimirova,et al.  Mitigation of Radiation Effects in SRAM-Based FPGAs for Space Applications , 2014, ACM Comput. Surv..

[9]  Qiang Xu,et al.  Approximate Computing: A Survey , 2016, IEEE Design & Test.

[10]  Daniel Ménard,et al.  The hidden cost of functional approximation against careful data sizing — A case study , 2017, Design, Automation & Test in Europe Conference & Exhibition (DATE), 2017.