一句话：提出模块化可配置的 DNN 加速器 MAERI，通过微小开关支持多种数据流映射，提高资源利用率。
深度神经网络加速器通常采用空间架构，由大量并行处理单元直接通信，以减少 DRAM 访问延迟与能耗。然而，多数加速器仅针对固定数据流模式进行 PE 与片上网络协同设计，难以高效映射卷积、循环、池化、全连接等不同层以及稠密/稀疏、不同分区方式带来的任意数据流，导致计算资源利用率不足。为此，本文提出 MAERI，一种由模块化、可配置构建块组成的 DNN 加速器。它通过配置微小开关，使内部互连能够支持多种 DNN 分区与数据流映射，从而提升可编程性。评估表明，在多种数据流映射下，MAERI 相比具有刚性 NoC 结构的基线，资源利用率提升 8% 至 459%。
Deep neural networks (DNN) have demonstrated highly promising results across computer vision and speech recognition, and are becoming foundational for ubiquitous AI. The computational complexity of these algorithms and a need for high energy-efficiency has led to a surge in research on hardware accelerators. % for this paradigm. To reduce the latency and energy costs of accessing DRAM, most DNN accelerators are spatial in nature, with hundreds of processing elements (PE) operating in parallel and communicating with each other directly. DNNs are evolving at a rapid rate, and it is common to have convolution, recurrent, pooling, and fully-connected layers with varying input and filter sizes in the most recent topologies.They may be dense or sparse. They can also be partitioned in myriad ways (within and across layers) to exploit data reuse (weights and intermediate outputs). All of the above can lead to different dataflow patterns within the accelerator substrate. Unfortunately, most DNN accelerators support only fixed dataflow patterns internally as they perform a careful co-design of the PEs and the network-on-chip (NoC). In fact, the majority of them are only optimized for traffic within a convolutional layer. This makes it challenging to map arbitrary dataflows on the fabric efficiently, and can lead to underutilization of the available compute resources. DNN accelerators need to be programmable to enable mass deployment. For them to be programmable, they need to be configurable internally to support the various dataflow patterns that could be mapped over them. To address this need, we present MAERI, which is a DNN accelerator built with a set of modular and configurable building blocks that can easily support myriad DNN partitions and mappings by appropriately configuring tiny switches. MAERI provides 8-459% better utilization across multiple dataflow mappings over baselines with rigid NoC fabrics.
DOI 原文 ·
@inproceedings{paperbot4233,
title = {MAERI},
author = {Hyoukjun Kwon and Ananda Samajdar and Tushar Krishna},
booktitle = {ASPLOS},
year = {2018},
doi = {10.1145/3173162.3173176}
}