Thread-level Parallelism in Fault Simulation of Deep Neural Networks on Multi-Processor Systems

High-performance fault simulation is one of the essential and preliminary tasks in the process of online and offline testing of machine learning (ML) hardware. Deep neural networks (DNN), as one of the essential parts of ML programs, are widely used in many critical and non-critical applications in Systems-on-Chip and ASIC designs. Through fault simulation for DNNs, by increasing the number of neurons, the fault simulation time increases exponentially. However, the software architecture of neural networks and the lack of dependency between neurons in each inference layer provide significant opportunity for parallelism of the fault simulation time in a multi-processor platform. In this paper, a multi-thread technique for hierarchical fault simulation of neural network is proposed, targeting both permanent and transient faults. During the process of fault simulation the neurons for each inference layer will be distributed among the executing threads. Since in the process of hierarchical fault simulation, the faulty neuron demands proportionally enormous computation comparing to behavioural model of non-faulty neurons, the faulty neuron will be assigned to one thread while the rest of the neurons will be divided among the remaining threads. Experimental results confirm the time efficiency of the proposed fault simulation technique on multi-processor architectures.