尝试设计用于优化SNN部署的成对Ising型最大熵模型集成代价函数
An Attempt to Devise a Pairwise Ising-Type Maximum Entropy Model Integrated Cost Function for Optimizing SNN Deployment
AI总结：
本研究提出基于Ising型成对交互框架的代价函数优化SNN部署，但初步实验表明该方法因平衡假设与硬件复杂性表现不佳，需进一步改进。
摘要：
脉冲神经网络（SNNs）模拟生物神经元的脉冲行为，通常部署在分布式内存的神经形态硬件上。SNN的部署通常需要划分网络，并将这些划分映射到硬件的处理单元上。然而，寻找最优部署配置是一个NP难问题，通常通过优化算法来解决。虽然一些目标（例如内存利用率和芯片数量）是静态的，但其他目标（例如通信延迟和能效）取决于网络的动态行为，因此需要具备动态感知的优化。为了解决这个问题，我们使用Ising型成对交互框架对SNN动态进行建模，将微观神经元交互与宏观网络行为联系起来。我们通过探索Ising模型的参数和配置空间来优化部署。我们在部署于sPyNNaker神经形态平台的两个SNN上评估了我们的方法。初步结果表明该方法表现不佳，可能是由于Ising模型的平衡假设和现实世界神经形态硬件的架构复杂性，突显了其当前表述的局限性。更新：所提出的方法基于平衡动态SNN假设，原论文未提及这一点。该论文需要在进一步实验后重新审查并重新上传。
英文摘要：
Spiking Neural Networks (SNNs) emulate the spiking behavior of biological neurons and are typically deployed on distributed-memory neuromorphic hardware. The deployment of a SNN usually requires partitioning the network and mapping these partitions onto the hardware's processing units. However, finding optimal deployment configurations is an NP-hard problem, often addressed through optimization algorithms. While some objectives (e.g., memory utilization and chip count) are static, others (e.g., communication latency and energy efficiency) depend on the network's dynamic behavior, necessitating dynamic-aware optimization. To address this, we model SNN dynamics using an Ising-type pairwise interaction framework, bridging microscopic neuron interactions with macroscopic network behavior. We optimize deployment by exploring the parameter and configuration spaces of the Ising model. We evaluate our approach on two SNNs deployed on the sPyNNaker neuromorphic platform. Initial results suggest that the method underperforms, potentially due to the Ising model's equilibrium assumptions and the architectural complexity of real-world neuromorphic hardware, highlighting limitations in its current formulation. Update: The method proposed is with a equilibrium-dynamics SNN assumption, and the original paper does not mention this. The paper needs to be revisited and reuploaded after further experiments.
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