Neural networks play a key role in modern AI accelerators, in which acceleration and power reduction have been two known issues. When human lives may be threatened by accidents due to a wrong decision, the reliability bursts out far beyond other is-sues of the AI accelerators in automotive chips. Although input-side deep layers have been shown to possess considerable self-healing, arithmetic faults in shallow decision layers may still cause unimaginable catastrophe. This motivates us to develop a synthe-sis system called TAIWAN Online for testing AI with AN codes online. TAIWAN Online takes a trained model with a Keras-like format and the accuracy resolution for predicting a suitable sub-system. For low-resolution datasets, a ternary-coded-binarized neural network called TCBNN is proposed for approximate com-puting, where AN codes are adopted for arithmetic-weight error correction. While for high-resolution datasets, redundant residue number systems are applied for parallelized acceleration, and AN codes are utilized as AN-RRNS for self-checking in efficient mul-tiple residue-modular redundancies, MMR. Briefly pointing out the key contributions, a k-moduli AN-RRNS can highly reduce the time-area product of MMR decoders from O(k4) of state-of-the-art RRNSs to only one. While the TCBNN can have fewer synapses than any regular-weight-quantized BNNs. From exper-imental results for a neuron-based block, the MTBF can be im-proved up to 126 times in the proposed infection-rate model.

fault-tolerant computing;neural network acceleration;error-correcting codes;AN codes;quantized neural network;redundant residue number system;automotive chips