With the increasing complexity of neural network models, the Network-on-Chip (NoC) has become a critical communication backbone in heterogeneous computing architectures. However, existing NoC simulation tools often fall short in supporting diverse computational units such as matrix processors and RISC-V programmable cores, limiting their ability to meet the stringent demands of real-time performance, high throughput, and energy efficiency in large-scale AI workloads. To overcome these limitations, this paper presents a behavior-level NoC simulation framework tailored for heterogeneous computing environments. The framework features precise node-level modeling, a dynamic pipelining mechanism,... More >

With the rapid advancement of deep learning, Spiking Neural Networks (SNNs) have attracted growing interest due to their low power consumption, sensitivity to temporal information, and biological plausibility. However, deploying SNNs in resource-constrained, real-time embedded environments presents significant challenges--chiefly their complex training processes, limited hardware acceleration support, and the difficulty of performing scheduling analysis. This paper presents an integrated modeling and scheduling analysis framework for SNNs based on the MARTE (Modeling and Analysis of Real-Time and Embedded Systems) standard defined by the OMG. Key SNN components--such as neurons, synapses, an... More >

In cognitive radio networks (CRNs), dynamic spectrum handoff requires efficient path planning to minimize the overhead of frequent channel switching. This paper proposes a polynomial-time approximation algorithm for spectrum handoff scheduling, based on an improved Traveling Salesman Problem (TSP) modeling of the channel switching sequence. A two-phase cooperative mechanism is designed to minimize frequency-hopping overhead. We rapidly generate diverse candidate channel-switching sequences using a probabilistic method guided by real-time spectrum availability distributions. We dynamically merge locally optimal sub-paths by leveraging historical channel quality data and predicted primary user... More >