PENDRAM：通过广义DRAM数据映射策略实现深度神经网络的高性能与高能效处理
PENDRAM: Enabling High-Performance and Energy-Efficient Processing of Deep Neural Networks through a Generalized DRAM Data Mapping Policy
AI总结：
PENDRAM提出一种广义DRAM数据映射策略及设计空间探索方法，通过优化CNN加速器的DRAM访问延迟与能耗，实现高性能高能效的嵌入式AI推理加速。
摘要：
卷积神经网络（CNNs）作为深度神经网络（DNNs）的一个重要类型，已成为解决机器学习任务的最先进方案。为了提高CNN推理的性能和能效，采用专用硬件加速器已成为普遍做法。然而，由于较高的片外存储器（DRAM）访问延迟和能耗，CNN加速器仍面临性能和能效挑战，这对于延迟和能耗受限的嵌入式应用尤为关键。此外，不同的DRAM架构具有不同的访问延迟和能耗特征，因此难以针对高性能和高能效的CNN加速器进行优化。为了解决这一问题，我们提出了PENDRAM，这是一种新颖的设计空间探索方法，通过广义DRAM数据映射策略实现高性能和高能效的CNN加速。具体而言，它探索了不同DRAM数据映射策略和DRAM架构在不同CNN划分和调度方案下对DRAM访问延迟和能耗的影响，然后识别出帕累托最优设计选择。实验结果表明，与其他映射策略相比，我们的DRAM数据映射策略将CNN加速器中DRAM访问的能量-延迟积提高了多达96%。通过这种方式，我们的PENDRAM方法能够在任意给定的DRAM架构下，为多样化的嵌入式AI应用提供高性能和高能效的CNN加速。
英文摘要：
Convolutional Neural Networks (CNNs), a prominent type of Deep Neural Networks (DNNs), have emerged as a state-of-the-art solution for solving machine learning tasks. To improve the performance and energy efficiency of CNN inference, the employment of specialized hardware accelerators is prevalent. However, CNN accelerators still face performance- and energy-efficiency challenges due to high off-chip memory (DRAM) access latency and energy, which are especially crucial for latency- and energy-constrained embedded applications. Moreover, different DRAM architectures have different profiles of access latency and energy, thus making it challenging to optimize them for high performance and energy-efficient CNN accelerators. To address this, we present PENDRAM, a novel design space exploration methodology that enables high-performance and energy-efficient CNN acceleration through a generalized DRAM data mapping policy. Specifically, it explores the impact of different DRAM data mapping policies and DRAM architectures across different CNN partitioning and scheduling schemes on the DRAM access latency and energy, then identifies the pareto-optimal design choices. The experimental results show that our DRAM data mapping policy improves the energy-delay-product of DRAM accesses in the CNN accelerator over other mapping policies by up to 96%. In this manner, our PENDRAM methodology offers high-performance and energy-efficient CNN acceleration under any given DRAM architectures for diverse embedded AI applications.
机构、总结、中文摘要均由AI分析而来，可能存在错误，请以论文正文为准。
