SIF-NPU: A 28nm 3.48 TOPS/W 0.25 TOPS/mm2CNN Accelerator with Spatially Independent Fusion for Real-Time UHD Super-Resolution

Sumin Lee, Ki Beom Lee, Sunghwan Joo, Hong Keun Ahn, Junghyup Lee, Dohyung Kim, Bumsub Ham, Seong Ook Jung

Research output: Chapter in Book/Report/Conference proceedingConference contribution

1 Citation (Scopus)

Abstract

This paper proposes a convolutional neural network (CNN)-based super-resolution accelerator for up-scaling to ultra-HD (UHD) resolution in real-Time in edge devices. A novel error-compensated bit quantization is adopted to reduce bit depth in the SR task. Spatially independent layer fusion is exploited to satisfy high throughput requirements at UHD resolution by increasing parallelism. Burst operation with write mask in the dual-port SRAM increases the process element utilization by allowing the concurrent multi-Access without exploiting additional memory. The accelerator is implemented in the 28nm technology and shows at least 4.3 times higher {FoM}({TOPS}/{mm}{2}\times {TOPS/W)} of 0.87 than the state-of-Art CNN accelerators. The implemented accelerator supports up-scaling up to 96 frames-per-seconds in UHD resolution.

Original languageEnglish
Title of host publicationESSCIRC 2022 - IEEE 48th European Solid State Circuits Conference, Proceedings
PublisherInstitute of Electrical and Electronics Engineers Inc.
Pages97-100
Number of pages4
ISBN (Electronic)9781665484947
DOIs
Publication statusPublished - 2022
Event48th IEEE European Solid State Circuits Conference, ESSCIRC 2022 - Milan, Italy
Duration: 2022 Sept 192022 Sept 22

Publication series

NameESSCIRC 2022 - IEEE 48th European Solid State Circuits Conference, Proceedings

Conference

Conference48th IEEE European Solid State Circuits Conference, ESSCIRC 2022
Country/TerritoryItaly
CityMilan
Period22/9/1922/9/22

Bibliographical note

Publisher Copyright:
© 2022 IEEE.

All Science Journal Classification (ASJC) codes

  • Hardware and Architecture
  • Electrical and Electronic Engineering
  • Electronic, Optical and Magnetic Materials
  • Instrumentation
  • Artificial Intelligence

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