神经网络到空间加速器的进化映射
Evolutionary Mapping of Neural Networks to Spatial Accelerators
AI总结：
本研究提出了一种基于进化算法的自动映射框架，用于神经形态加速器，实现了神经网络部署的低延迟和高能效。
摘要：
由计算-内存集成单元组成的空间加速器提供了一个吸引人的平台，用于部署具有低延迟和低能耗的推理工作负载。然而，充分利用其架构优势通常需要仔细的、由专家驱动的计算图到分布式处理元件的映射。在本工作中，我们通过将映射挑战框架为一个黑盒优化问题来自动化这一过程。我们介绍了首个针对神经形态加速器的进化、硬件在环映射框架，使没有深入硬件知识的用户能够更高效地部署工作负载。我们评估了我们的方法在Intel Loihi 2上，这是一个具有每芯片152个核心的2D网格代表性的空间加速器。我们的方法在两个稀疏多层感知机网络上，相比默认启发式方法总延迟减少了高达35%。此外，我们展示了我们的方法在多芯片系统中的可扩展性，并观察到能耗效率提高了高达40%，而无需显式优化。
英文摘要：
Spatial accelerators, composed of arrays of compute-memory integrated units, offer an attractive platform for deploying inference workloads with low latency and low energy consumption. However, fully exploiting their architectural advantages typically requires careful, expert-driven mapping of computational graphs to distributed processing elements. In this work, we automate this process by framing the mapping challenge as a black-box optimization problem. We introduce the first evolutionary, hardware-in-the-loop mapping framework for neuromorphic accelerators, enabling users without deep hardware knowledge to deploy workloads more efficiently. We evaluate our approach on Intel Loihi 2, a representative spatial accelerator featuring 152 cores per chip in a 2D mesh. Our method achieves up to 35% reduction in total latency compared to default heuristics on two sparse multi-layer perceptron networks. Furthermore, we demonstrate the scalability of our approach to multi-chip systems and observe an up to 40% improvement in energy efficiency, without explicitly optimizing for it.
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