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Few studies have explored the complex circuit simulation of stochastic and unary computing systems, which are referred to under the umbrella term of bit-stream processing. The computer simulation of multi-level cascaded circuits with reconvergent paths has not been largely examined in the context of bit-stream processing systems. This study addresses this gap and proposes a contingency table-based reconvergent path-aware simulation method for fast and efficient simulation of multi-level circuits. The proposed method exhibits significantly better runtime and accuracy. 

Quantum computers in the current noisy intermediate-scale quantum (NISQ) era face two major limitations - size and error vulnerability. Although quantum error correction (QEC) methods exist, they are not applicable at the current size of computers, requiring thousands of qubits, while NISQ systems have nearly one hundred at most. One common approach to improve reliability is to adjust the compilation process to create a more reliable final circuit, where the two most critical compilation decisions are the qubit allocation and qubit routing problems. We focus on solving the qubit allocation problem and identifying initial layouts that result in a reduction of error. To identify these layouts, we combine reinforcement learning with a graph neural network (GNN)-based Q-network to process the mesh topology of the quantum computer, known as the backend, and make mapping decisions, creating a Graph Neural Network Assisted Quantum Compilation (GNAQC) strategy. We train the architecture using a set of four backends and six circuits and find that GNAQC improves output fidelity by roughly 12.7% over pre-existing allocation methods. 

Sorting is a fundamental function in many applications from data processing to database systems. For high performance, sorting-hardware based sorting designs are implemented by conventional binary or emerging stochastic computing (SC) approaches. Binary designs are fast and energy-efficient but costly to implement. SC-based designs, on the other hand, are area and power-efficient but slow and energy-hungry. So, the previous studies of the hardware-based sorting further faced scalability issues. In this work, we propose a novel scalable low-cost design for implementing sorting networks. We borrow the concept of SC for the area- and power efficiency but use weighted stochastic bit-streams to address the high latency and energy consumption issue of SC designs. A new lock and swap (LAS) unit is proposed to sort weighted bit-streams. The LAS-based sorting network can determine the result of comparing different input values early and then map the inputs to the corresponding outputs based on shorter weighted bit-streams. Experimental results show that the proposed design approach achieves much better hardware scalability than prior work. Especially, as increasing the number of inputs, the proposed scheme can reduce the energy consumption by about 3.8% - 93% compared to prior binary and SC-based designs. 

Stochastic computing (SC) is an alternative computing paradigm that possesses data in the form of long uniform bit-streams rather than conventional compact weighted binary numbers. SC is fault-tolerant and can compute on small, efficient circuits, promising advantages over conventional arithmetic for smaller computer chips. SC has been primarily used in scientific research, not in practical applications. Digital sound source localization (SSL) is a useful signal processing technique that locates speakers using multiple microphones in cell phones, laptops, and other voice-controlled devices. SC has not been integrated into SSL in practice or theory. In this work, for the first time to the best of our knowledge, we implement an SSL algorithm in the stochastic domain and develop a functional SC-based sound source localizer. The developed design can replace the conventional design of the algorithm. The practical part of this work shows that the proposed stochastic circuit does not rely on conventional analog-to-digital conversion and can process data in the form of pulss-width-mudulated (PWM) signals.