## 2 Related Work
Another efficient, user-friendly, and GPU-accelerated library for simulating large-scale SNN models with a high degree of biological detail is called CARL-sim [32]. It allows executing networks of Izhikevich spiking neurons with realistic synaptic dynamics on both CPUs and GPUs. CARLsim offers a PyNN-like programming interface in C/C++, efficient and scalable simulation, support for various biological features, e.g., STDP and short-term plasticity (STP), functionality for creating complex networks with spatial structure, extensive documentation, and open-source nature. Developed and maintained by researchers at the Cognitive Anteater Robotics Laboratory (CARL) at the University of California, Irvine, CARLsim has been continuously improved since its introduction in 2009. It is also one of the first open-source simulation systems to utilize GPUs to accelerate SNN simulations. Moreover, the work in [33] introduces Spyx, an open-source library for simulating SNNs using JAX, a high-performance numerical computing library developed by Google. By leveraging JAX and the neural network library Haiku, Spyx enables the creation and simulation of SNNs with a focus on flexibility and computational efficiency. It provides a flexible framework for building and simulating SNNs, allowing users to define custom neuron models, synaptic dynamics, and learning rules. The authors in [34] recently introduced a Neural Event-based Simulator for Real-Time (NESIM-RT), a unique SNN simulator designed for real-time distributed simulations. Its key features include real-time simulation capabilities, a distributed architecture for handling large-scale SNNs, and efficient performance and scalability. NESIM-RT's real-time simulation sets it apart from many other popular SNN simulators, making it suitable for applications in robotics, neuromorphic computing, and brain-computer interfaces that require real-time SNN simulations. Also, recently, the authors in [17] proposed SWsnn. This novel SNN simulator addresses the high memory access time issue in SNN simulations by leveraging the Sunway SW26010 pro processor's architecture. It features six core groups, each with 16 MB of local data memory with high-speed read and write characteristics, making it suitable for efficiently performing SNN simulation tasks and outperforming other mainstream GPU-based simulators for specific scales of neural networks. To enable larger-scale SNN simulations, SWsnn employs a simulation computation based on a large, shared model of the Sunway processor and has developed a multiprocessor version of the simulator using this mode. This mode demonstrates its capability to handle more extensive SNN simulations than single-processor implementation. Recently, the authors in [35] introduced SHIP (Spiking (neural network) Hardware In PyTorch), a computational framework for simulating and validating novel technologies in hardware SNNs. SHIP is developed in Python and uses PyTorch as the backend, allowing it to leverage PyTorch's optimized libraries for fast matrix calculations, network optimization algorithms, GPU acceleration, and other advantages. It aims to support investigating and validating novel technologies within SNN architectures. SHIP simulates a network as a collection of interoperating groups of components (neurons, synapses, etc. ), with each group supported by a model defined as a Python class to facilitate the algorithmic definition of component models. The temporal progress follows a fixed time-step clock-driven algorithm, and the data flow management relies on defining a stack of the groups to optimize efficiency. Key features include an easy-to-learn interface, low computational requirements, facilitated development of user-defined models, access to time-dependent parameters and results, methods enabling offline and online training, and suitability for parameter-dependent simulations. Using SHIP, their paper demonstrates the building, simulation, and training of SNNs on tasks like spoken digit recognition, Braille character classification, and ECG arrhythmia detection.