提出动态调度算法，提升多处理器内存计算系统中CNN的运行效率
Efficient Deployment of CNN Models on Multiple In-Memory Computing Units
- 设计LBLP算法，动态分配CNN节点到多个处理单元
- 相比其他策略，处理速率提升最高达2.1倍，延迟降低显著
- 适合需要低延迟部署CNN的边缘计算场景
内存计算（IMC）通过缓解数据搬运瓶颈并利用基于内存的并行计算特性，为深度学习加速带来范式变革。在基于IMC的硬件上高效部署卷积神经网络（CNN）模型，需采用先进的任务分配策略以实现最大计算效率。本文使用支持多处理单元（PU）的IMC模拟器（IMCE），研究在多处理器系统中部署CNN模型对处理速率与延迟的影响。为此，提出负载均衡-最长路径（LBLP）算法，动态将所有CNN节点分配至可用的IMCE PU，以最大化处理速率并最小化延迟。在多种CNN模型上的基准测试表明，该算法显著优于其他调度策略。
原文摘要 · Abstract (English)
In-Memory Computing (IMC) represents a paradigm shift in deep learning acceleration by mitigating data movement bottlenecks and leveraging the inherent parallelism of memory-based computations. The efficient deployment of Convolutional Neural Networks (CNNs) on IMC-based hardware necessitates the use of advanced task allocation strategies for achieving maximum computational efficiency. In this work, we exploit an IMC Emulator (IMCE) with multiple Processing Units (PUs) for investigating how the deployment of a CNN model in a multi-processing system affects its performance, in terms of processing rate and latency. For that purpose, we introduce the Load-Balance-Longest-Path (LBLP) algorithm, that dynamically assigns all CNN nodes to the available IMCE PUs, for maximizing the processing rate and minimizing latency due to efficient resources utilization. We are benchmarking LBLP against other alternative scheduling strategies for a number of CNN models and experimental results demonstrate the effectiveness of the proposed algorithm.
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