AppRoximaTivE Flexible Circuits and Computing for IoT – ARTEFaCT

Energy issues are central to the ICT industry, and especially to the evolution of the Internet of Things (IoT). Current low-power design techniques cannot support the estimated growth in number IoT objects and in the same time keep the ICT energy consumption within sustainable bounds. This project aims to build on the preliminary results on inexact and exact near-threshold and sub-threshold circuit design to achieve major energy consumption reductions by enabling adaptive accuracy control of applications.
ARTEFaCT proposes to address, in a consistent fashion, the entire design stack, from physical hardware design, up to software application analysis, compiler optimizations, and dynamic energy management. We do believe that combining sub-near-threshold with inexact circuits on the hardware side and, in addition, extending this with intelligent and adaptive power management on the software side will produce outstanding results in terms of energy reduction, i.e., at least one order of magnitude, in IoT.
The project will contribute along three research directions:
- 1) approximate, ultra low-power circuit design,
- 2) modeling and analysis of variable levels of computation precision in applications, and
- 3) accuracy-energy trade-offs in software.
The expertise of the consortium members is complementary and covers all required technical knowledge necessary to reach our objectives, i.e., ultra low power hardware design (EPFL, CSEM, CEA Leti), formal analysis of precision in algorithms (INRIA, INSA), and static and dynamic energy management (INRIA, CEA Leti). The main benefits of ARTEFaCT will come from its inter-disciplinary character. The partners will closely collaborate on all three research directions of the project. Finally, the proof of concept will consist of an adaptive inexact test-chip in 180 nm or 65 nm processes. Several software use-cases relevant for the IoT domain will be considered, e.g., embedded vision, IoT sensors data fusion, to practically demonstrate the benefits of our approach.