Modern handheld devices often employ neural processing units (NPUs) to accelerate deep neural network (DNN) inference applications. Unlike the AI accelerator of a data center, the NPU of an edge device has strict price and energy budgets. In an NPU, DRAM memory consumes much energy due to frequent data movement across on-chip and off-chip memory. The inter-layer operator scheduling has been shown to reduce off-chip memory transactions by reusing DNN operator outputs on the on-chip memory space. However, it often deeply traverses operators and substantially increases off-chip memory data traffic when the on-chip memory of an NPU decreases in size. Consequently, this work creates a DNN model compilation framework called EDA that transparently improves the energy efficiency of an NPU by adjusting the operator traversal depth of the inter-layer operator scheduling in a stacked DNN model. First, EDA transforms a DNN model into a tensor-splitting model. Second, EDA breaks the tensor-splitting model graph into multiple subgraphs. Third, the EDA inter-layer cost model quickly determines the depth of each subgraph. Fourth, EDA properly manages the on-chip shared memory space of an NPU to avoid overusing memory space. Finally, EDA devises the operator grouping method to improve the MAC unit and on-chip memory space utilization. EDA improves the geometric means of $2.08 \times$ and $2.39 \times$, respectively, in energy efficiency and performance over the NPU with designated memory buffers.

In this study, we learned more about quantum properties from quantum and metaheuristic algorithms after conducting many experiments. We then propose an improved version of Quantum-inspired Tabu Search (QTS), which en-hances the utilization of population information. The new version is called “Amplitude-Ensemble” QTS (AE-QTS). This makes AE-QTS more similar to the real quantum search algorithm, Grover Search Algorithm, in abstract concept, while keeping the simplicity of the algorithm. Later, we demonstrate the AE-QTS on the classical combinatorial optimization 0/1 knapsack problem. Experimental results show that the AE-QTS outperforms other algorithms, including the QTS, by at least an average increase of nearly 20% in all cases and even by 30% in some cases. Even as the problem complexity increases, the quality of the solutions found by our method remains superior to that of the QTS. These results confirm that our method has better search performance.