Hardware implementation of neuromorphic computing can significantly improve performance and energy efficiency of machine learning tasks implemented with spiking neural networks (SNNs), making these hardware platforms particularly suitable for embedded systems and other energy-constrained environments. We observe that the long bitlines and wordlines in a crossbar of the hardware create significant current variations when propagating spikes through its synaptic elements, which are typically designed with non-volatile memory (NVM). Such current variations create a thermal gradient within each crossbar of the hardware, depending on the machine learning workload and the mapping of neurons and synapses of the workload to these crossbars. \mr{This thermal gradient becomes significant at scaled technology nodes and it increases the leakage power in the hardware leading to an increase in the energy consumption.} We propose a novel technique to map neurons and synapses of SNN-based machine learning workloads to neuromorphic hardware. We make two novel contributions. First, we formulate a detailed thermal model for a crossbar in a neuromorphic hardware incorporating workload dependency, where the temperature of each NVM-based synaptic cell is computed considering the thermal contributions from its neighboring cells. Second, we incorporate this thermal model in the mapping of neurons and synapses of SNN-based workloads using a hill-climbing heuristic. The objective is to reduce the thermal gradient in crossbars. We evaluate our neuron and synapse mapping technique using 10 machine learning workloads for a state-of-the-art neuromorphic hardware. We demonstrate an average 11.4K reduction in the average temperature of each crossbar in the hardware, leading to a 52% reduction in the leakage power consumption (11% lower total energy consumption) compared to a performance-oriented SNN mapping technique.

中文翻译：

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尖峰神经网络到神经形态硬件的热感知编译

神经拟态计算的硬件实现可以显着提高使用尖峰神经网络 (SNN) 实现的机器学习任务的性能和能源效率，使这些硬件平台特别适用于嵌入式系统和其他能源受限的环境。我们观察到，当通过其突触元件传播尖峰时，硬件交叉开关中的长位线和字线会产生显着的电流变化，突触元件通常采用非易失性存储器 (NVM) 设计。这种电流变化会在硬件的每个交叉开关内产生热梯度，这取决于机器学习工作负载以及工作负载的神经元和突触到这些交叉开关的映射。\mr{这种热梯度在缩放技术节点上变得显着，它增加了硬件中的泄漏功率，导致能耗增加。}我们提出了一种新技术，将基于 SNN 的机器学习工作负载的神经元和突触映射到神经形态硬件。我们做出了两项新颖的贡献。首先，我们为包含工作负载依赖性的神经形态硬件中的横杆制定了一个详细的热模型，其中每个基于 NVM 的突触单元的温度是根据其相邻单元的热贡献计算的。其次，我们使用爬山启发式将该热模型结合到基于 SNN 的工作负载的神经元和突触的映射中。目标是降低横杆中的热梯度。我们使用 10 个机器学习工作负载评估我们的神经元和突触映射技术，用于最先进的神经形态硬件。我们证明了硬件中每个交叉开关的平均温度平均降低了 11.4K，与面向性能的 SNN 映射技术相比，泄漏功耗降低了 52%（总能耗降低了 11%）。