Near Threshold Voltage (NTV) computing has been recently proposed as a technique to save energy, at the cost of incurring higher error rates including, among others, Silent Data Corruption (SDC). In this paper, we evaluate the energy efficiency of dense linear algebra routines using several low-power multicore processors and we analyze whether the potential energy reduction achieved when scaling the processor to operate at a low voltage compensates the cost of integrating a fault tolerance mechanism that tackles SDC. Our study targets algorithmic-based fault-tolerant versions of the dense matrix-vector and matrix(-matrix) multiplication kernels (gemv and gemm, respectively), using the BLIS framework, as well as an implementation of the LU factorization with partial pivoting built on top of gemm. Furthermore, we tailor the study for a number of representative 32-bit and 64-bit multicore processors from ARM that were specifically designed for energy efficiency.
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