Everything Leaves Footprints: Hardware Accelerated Intermittent Deep Inference

Chih Kai Kang, Hashan Roshantha Mendis, Chun Han Lin, Ming Syan Chen, Pi Cheng Hsiu*

*Corresponding author for this work

Research output: Contribution to journalArticlepeer-review

27 Citations (Scopus)


Current peripheral execution approaches for intermittently powered systems require full access to the internal hardware state for checkpointing or rely on application-level energy estimation for task partitioning to make correct forward progress. Both requirements present significant practical challenges for energy-harvesting, intelligent edge Internet-of-Things devices, which perform hardware-accelerated deep neural network (DNN) inference. Sophisticated compute peripherals may have an inaccessible internal state, and the complexity of DNN models makes it difficult for programmers to partition the application into suitably sized tasks that fit within an estimated energy budget. This article presents the concept of inference footprinting for intermittent DNN inference, where accelerator progress is accumulatively preserved across power cycles. Our middleware stack, HAWAII, tracks and restores inference footprints efficiently and transparently to make inference forward progress, without requiring access to the accelerator internal state and application-level energy estimation. Evaluations were carried out on a Texas Instruments device, under varied energy budgets and network workloads. Compared to a variety of task-based intermittent approaches, HAWAII improves the inference throughput by 5.7%-95.7%, particularly achieving higher performance on heavily accelerated DNNs.

Original languageEnglish
Article number9211553
Pages (from-to)3479-3491
Number of pages13
JournalIEEE Transactions on Computer-Aided Design of Integrated Circuits and Systems
Issue number11
Publication statusPublished - 2020 Nov


  • Deep neural networks (DNNs)
  • edge computing
  • energy harvesting
  • intermittent systems

ASJC Scopus subject areas

  • Software
  • Computer Graphics and Computer-Aided Design
  • Electrical and Electronic Engineering


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