Computer Science > Hardware Architecture
[Submitted on 14 Jan 2021 (v1), last revised 1 Sep 2021 (this version, v3)]
Title:Enabling Large Neural Networks on Tiny Microcontrollers with Swapping
View PDFAbstract:Running neural networks (NNs) on microcontroller units (MCUs) is becoming increasingly important, but is very difficult due to the tiny SRAM size of MCU. Prior work proposes many algorithm-level techniques to reduce NN memory footprints, but all at the cost of sacrificing accuracy and generality, which disqualifies MCUs for many important use cases. We investigate a system solution for MCUs to execute NNs out of core: dynamically swapping NN data chunks between an MCU's tiny SRAM and its large, low-cost external flash. Out-of-core NNs on MCUs raise multiple concerns: execution slowdown, storage wear out, energy consumption, and data security. We present a study showing that none is a showstopper; the key benefit -- MCUs being able to run large NNs with full accuracy and generality -- triumphs the overheads. Our findings suggest that MCUs can play a much greater role in edge intelligence.
Submission history
From: Hongyu Miao [view email][v1] Thu, 14 Jan 2021 21:38:57 UTC (5,461 KB)
[v2] Fri, 5 Feb 2021 15:58:50 UTC (5,461 KB)
[v3] Wed, 1 Sep 2021 14:53:54 UTC (6,365 KB)
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