# Exploring Computation-Communication Tradeoffs in Camera Systems

## TL;DR

This paper investigates the design of energy-efficient camera systems that balance computation and communication, demonstrating two case studies with significant performance and energy improvements for real-time vision tasks.

## Contribution

It introduces two novel camera system architectures optimized for energy and performance, highlighting the importance of early data reduction in in-camera processing.

## Key findings

- Energy-harvested face authentication camera achieves better efficiency than general-purpose microprocessors.
- Multi-FPGA pipeline outperforms CPU and GPU in real-time 3D-360 VR video processing.
- Early data reduction is crucial for optimizing in-camera computation and communication.

## Abstract

Cameras are the defacto sensor. The growing demand for real-time and low-power computer vision, coupled with trends towards high-efficiency heterogeneous systems, has given rise to a wide range of image processing acceleration techniques at the camera node and in the cloud. In this paper, we characterize two novel camera systems that use acceleration techniques to push the extremes of energy and performance scaling, and explore the computation-communication tradeoffs in their design. The first case study targets a camera system designed to detect and authenticate individual faces, running solely on energy harvested from RFID readers. We design a multi-accelerator SoC design operating in the sub-mW range, and evaluate it with real-world workloads to show performance and energy efficiency improvements over a general purpose microprocessor. The second camera system supports a 16-camera rig processing over 32 Gb/s of data to produce real-time 3D-360 degree virtual reality video. We design a multi-FPGA processing pipeline that outperforms CPU and GPU configurations by up to 10x in computation time, producing panoramic stereo video directly from the camera rig at 30 frames per second. We find that an early data reduction step, either before complex processing or offloading, is the most critical optimization for in-camera systems.