Investigating data representation for efficient and reliable Convolutional Neural Networks

Nowadays, Convolutional Neural Networks (CNNs) are widely used as prediction models in different fields, with intensive use in real-time safety-critical systems. Recent studies have demonstrated that hardware faults induced by an external perturbation or aging effects, may significantly impact the CNN inference, leading to prediction failures. Therefore, ensuring the reliability of CNN platforms is crucial, especially when deployed in critical applications. A lot of effort has been made to reduce the memory and energy footprint of CNNs, paving the way to the adoption of approximate computing techniques such as quantization, reduced precision, weight sharing, and pruning. Unfortunately, approximate computing reduces the intrinsic redundancy of CNNs making them more efficient but less resilient to hardware faults. The goal of this work is twofold. First, we assess the reliability of a CNN when reduced bit widths and two different data types (floating- and fixed-point) are used to represent the network parameters (i.e., synaptic weights). Second, we intend to investigate the best compromise between data type, bit-widths reduction, and reliability. The characterization is performed through a fault injection environment built on the darknet open-source framework and targets two CNNs: LeNet-5 and YOLO. Experimental results show that fixed-point data provide the best trade-off between memory footprint reduction and CNN resilience. In particular, for LeNet-5, we achieved a 4X memory footprint reduction at the cost of a slightly reduced reliability (0.45% of critical faults) without retraining the CNN.