The public defense of Mohammad Loni's doctoral thesis in Computer Science
The public defense of Mohammad Loni's doctoral thesis "Efficient Design of Scalable Deep Neural Networks for Resource-Constrained Edge Devices" will take place at Mälardalen University, room Delta (Västerås Campus) and online at 13:30 on 13th October 2022.
Titel: "Efficient Design of Scalable Deep Neural Networks for Resource-Constrained Edge Devices"
The faculty examiner is Professor Franz Pernkopf, GUT, Austria. The examining committee consists of Assoc. Professor Farshad Khunjush, Shiraz University, Iran; Professor Vladimir Vlassov, KTH, Sweden and Professor Andreas Ermedahl, KTH, Sweden.
Reserve is Professor Shahina Begum, MDU, Sweden.
The doctoral thesis has serial number 363.
Deep Neural Networks (DNNs) are increasingly being processed on resource-constrained edge nodes (computer nodes used in, e.g., cyber-physical systems or at the edge of computational clouds) due to efficiency, connectivity, and privacy concerns. This thesis investigates and presents new techniques to design and deploy DNNs for resource-constrained edge nodes. We have identified two major bottlenecks that hinder the proliferation of DNNs on edge nodes: (i) the significant computational demand for designing DNNs that consumes a low amount of resources in terms of energy, latency, and memory footprint; and (ii) further conserving resources by quantizing the numerical calculations of a DNN provides remarkable accuracy degradation.
To address (i), we present novel methods for cost-efficient Neural Architecture Search (NAS) to automate the design of DNNs that should meet multifaceted goals such as accuracy and hardware performance. To address (ii), we extend our NAS approach to handle the quantization of numerical calculations by using only the numbers -1, 0, and 1 (so-called ternary DNNs), which achieves higher accuracy. Our experimental evaluation shows that the proposed NAS approach can provide a 5.25× reduction in design time and up to 44.4× reduction in network size compared to state-of-the-art methods. In addition, the proposed quantization approach delivers 2.64% higher accuracy and 2.8× memory saving compared to full-precision counterparts with the same bit-width resolution. These benefits are attained over a wide range of commercial-off-the-shelf edge nodes showing this thesis successfully provides seamless deployment of DNNs on resource-constrained edge nodes.