Euphrates: Algorithm-SoC Co-Design for Low-Power Mobile Continuous Vision

Continuous computer vision (CV) tasks increasingly rely on convolutional neural networks (CNN). However, CNNs have massive compute demands that far exceed the performance and energy constraints of mobile devices. In this paper, we propose and develop an algorithm-architecture co-designed system, Eup...

Full description

Saved in:
Bibliographic Details
Published in:Proceedings - International Symposium on Computer Architecture pp. 547 - 560
Main Authors: Zhu, Yuhao, Samajdar, Anand, Mattina, Matthew, Whatmough, Paul
Format: Conference Proceeding
Language:English
Published: IEEE 01.06.2018
Subjects:
Cameras
Computer architecture
Computer Vision
Engines
Extrapolation
ISP
Microsoft Windows
Mobile Computing
Neural Network
Two dimensional displays
ISSN:2575-713X
Online Access:Get full text
Tags: Add Tag
No Tags, Be the first to tag this record!
Description
Summary:Continuous computer vision (CV) tasks increasingly rely on convolutional neural networks (CNN). However, CNNs have massive compute demands that far exceed the performance and energy constraints of mobile devices. In this paper, we propose and develop an algorithm-architecture co-designed system, Euphrates, that simultaneously improves the energyefficiency and performance of continuous vision tasks. Our key observation is that changes in pixel data between consecutive frames represents visual motion. We first propose an algorithm that leverages this motion information to relax the number of expensive CNN inferences required by continuous vision applications. We co-design a mobile System-ona-Chip (SoC) architecture to maximize the efficiency of the new algorithm. The key to our architectural augmentation is to co-optimize different SoC IP blocks in the vision pipeline collectively. Specifically, we propose to expose the motion data that is naturally generated by the Image Signal Processor (ISP) early in the vision pipeline to the CNN engine. Measurement and synthesis results show that Euphrates achieves up to 66% SoC-level energy savings (4× for the vision computations), with only 1% accuracy loss.
ISSN:2575-713X
DOI:10.1109/ISCA.2018.00052