X-DNNs: Systematic Cross-Layer Approximations for Energy-Efficient Deep Neural Networks

Muhammad Abdullah Hanif, Alberto Marchisio, Tabasher Arif, Rehan Hafiz, Semeen Rehman, Muhammad Shafique

Research output: Contribution to journalArticlepeer-review

Abstract

Growing interest towards the development of smart Cyber Physical Systems (CPS) and Internet of Things (IoT) has motivated the researchers to explore the suitability of carrying out embedded machine learning. This has enabled a new age of smart CPS and IoT with emerging applications like autonomous vehicles, smart cities and houses, advanced robotics, IoT-Healthcare, and Industry 4.0. Due to the availability of a huge amount of data and compute power, Deep Neural Networks (DNNs) have become one of the enabling technologies behind this current age of machine learning and intelligent systems. The benefits of DNNs however come at a high computational cost and require tremendous amount of energy/power resources that are typically not available on (embedded) IoT and CPS devices, especially when considering the IoT-Edge nodes. To improve the performance and energy/power efficiency of these DNNs, this paper presents a cross-layer approximation methodology which exploits the error resiliency offered by DNNs at various hardware and software layers of the computing stack. We present various case studies at both software and hardware level in order to demonstrate the energy benefits of the proposed methodology. At software level we provide a systematic pruning methodology while at hardware level we provide a case study utilizing approximation of multipliers used for performing the weighted sum operation in the neural processing of DNNs.

Original languageEnglish (US)
Pages (from-to)520-534
Number of pages15
JournalJournal of Low Power Electronics
Volume14
Issue number4
DOIs
StatePublished - Dec 2018

Keywords

  • Accelerator
  • Approximate computing
  • Approximate multiplier
  • Artificial intelligence
  • Co-design
  • Cross-layer
  • Deep neural networks
  • Embedded learning
  • Energy-efficient design
  • Error resilience
  • Hardware
  • Machine learning
  • Modeling
  • Optimization
  • Pruning
  • Quantization

ASJC Scopus subject areas

  • Electrical and Electronic Engineering

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