Analyzing the Reliability of Stream Sparse Matrix-Vector Multiplication Accelerators: A High-Level Approach

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Today, advances in scientific and embedded computing and the incredible proliferation of machine learning algorithms take advantage of specialized hardware accelerators to provide exceptional performance and good accuracy. In some safety-critical applications like autonomous robotics, healthcare, and automotive, crucial mathematical operations such as convolutions are often efficiently mapped in hardware as Matrix-Vector(MxV) and Matrix-Matrix (MxM) multiplications. Moreover, sophisticated sparsity algorithms aim to improve performance and power consumption. Unfortunately, technology scaling trends may increase the proliferation of faults on hardware during the in-field operation, so there is a rising interest in the reliability assessment and analysis of sparsity-based accelerators. This work evaluates the impact of soft errors (bit-flips) on sparse-matrix dense-vector multiplication (SpMV) cores for safety-critical tactile sensing applications by resorting to a High-Level Synthesis (HLS) strategy. The experiments are performed on an open-source streaming SpMV core using the Compressed Sparse Row (CSR) format when processing characteristic medium-size sparse matrices (100x100 and 494x494). Our results indicate that data-path pipeline registers in the (SpMV) core are resilient to transient faults (< 1.3% of observed corruption effects), while large magnitude errors can be associated with the type of sparse matrix describing the system (e.g., number of non-zero values) and the type and features of the input vector sensor.