Satisfiability Modulo Theory (SMT) solvers are a useful tool that can be applied to a variety of problems, such as configuring relationships in distributed systems, detecting race conditions, and program analysis. String constraints are particularly difficult for SMT solvers to navigate, as the search space is generally large. Often times, classical SMT solvers will have to quit generating a solution for string constraints because it takes too long to find the solution. Quantum computing offers the advantages of quantum mechanics (e.g., superposition), which allows a system to explore a large search space much more efficiently. In this work, we explore creating a quantum-enabled SMT solver for string theory by using quantum annealing and Quadratic Unconstrained Binary Optimization (QUBO). Our preliminary results demonstrate that it is feasible to transform these string constraints to QUBO, and generate solutions for given constraints.

On-device learning is essential to achieve intelligence at the edge, where it is desirable to learn from few samples or even just a single sample. Memory-augmented neural networks (MANNs), which augment neural networks with an attentional memory, can draw on already learnt knowledge patterns and adapt to new but similar tasks. Implementing MANNs on conventional architectures can require a significant amount of costly data transfer, thereby limiting the practical use of MANNs at the edge. In this paper, we introduce algorithm/hardware co-design solutions which exploit compact designs of content addressable memories (CAMs) based on emerging non-volatile memories (e.g., FeFETs) to implement energy-efficient MANN accelerators. The design space of MANN accelerators is systematically analyzed by considering different circuit, architecture, and algorithm options. We further discuss how hyper-dimensional representations of data can be combined with MANNs to overcome the negative effect of device/circuit variabilities on learning quality, thus achieving not only energy-efficient but also accuracy-competitive on-device learning at the edge. We also investigate modeling of device-to-device (D2D) variation in FeFETs using the write-with-verify approach and detail its impact on the energy, delay, and accuracy of the MANN application.