부산대학교 도서관

In this article, a probabilistic interpolation recoder (PIR) circuit is developed for deep belief networks (DBNs) with probabilistic spin logic (p-bit)-based neurons. To verify the functionality and evaluate the performance of the PIRs, we have implemented a $784 \times 200 \times 10$784×200×10 DBN circuit in SPICE for a pattern recognition application using the MNIST dataset. The PIR circuits are leveraged in the last hidden layer to interpolate the probabilistic output of the neurons, which are representing different output classes, through sampling the p-bit's output values and then counting them in a defined sampling time window. The PIR circuit is proposed as an alternative for conventional interpolation methods which were based on using a resistor-capacitor tank to integrate each neuron's output, followed by an analog-to-digital converter to generate the digital output. The circuit simulation results of PIR circuit exhibit at least 54, 81, and 78 percent reductions in power, energy, and energy-error-product, respectively, compared to previous techniques, without using any of the area-consuming analog components in the interpolation circuit. In addition, PIR circuits provide an inherent single stuck-at fault tolerant feature to mitigate both transient and permanent faults at the circuit's output. Reliability properties of the PIR circuits for single stuck-at faults are shown to be enhanced relative to conventional interpolation without requiring hardware redundancy.