Dependable Deep Learning for Safety-Critical Airborne Embedded Systems
This project addresses design methods for the use of DNNs in airborne safety-critical systems. DNNs cannot rely on traditional design assurance techniques described in documents from certification authorities or standardization bodies. In this project, the research focus is on mitigation techniques for design errors in both hardware and software and for adversarial effects which can lead to system failures. The expected results are design methodologies and fault tolerant architectures for airborne safety-critical applications using neural networks.
Developing Predictable Vehicle Software Utilizing Time Sensitive Networking
Recent advancement in the functionality and new customer features in modern vehicles, especially autonomous vehicles, requires massive computational power and high-bandwidth on-board real-time communication. While there is a lot of research done to meet the challenge of computational requirements, relatively small efforts have been spent to deal with the challenge of supporting the high-bandwidth onboard communication requirements. In a recent effort to support high-bandwidth low-latency onboard real-time communication in modern vehicles, the IEEE Time-Sensitive Networking (TSN) task group has developed a set of standards targeting different classes of real-time traffic, support for time-triggered traffic at the same time as non-real-time traffic, support for resource reservation for different classes of traffic, support for clock synchronization, and providing several traffic shapers. However, a complete development support including modelling, timing analysis, configuration, deployment and execution for vehicular applications that use TSN is still missing from the state of the art. Since TSN is a complex technology with many options and configuration possibilities; taking full advantage of TSN in execution of complex vehicular functions is daunting task. This project aims at developing innovative techniques to provide a full-fledged development environment for vehicular applications that use TSN as the backbone for on-board communication. One unique characteristic of the project consortium is that it offers a clear value chain from academia (MDH), through the tool developer/vendor (Arcticus), and to the end user of the technology (Volvo), who will use the techniques and tools to develop prototype vehicles.
Heterogeneous systems - software-hardware integration
Automatic Design of Safe, High-Performance and Compact Deep Learning Models for Autonomous Vehicles
Integrated Time Sensitive Networking and Legacy Communications in Predictable Vehicle-platforms
Predictable Software Development in Connected Vehicles Utilising Blended TSN-5G Networks
Deep Learning Accelerator on Commercial Programmable Devices
DeepMaker aims to provide a framework to generate synthesizable accelerators of Deep Neural Networks (DNNs) that can be used for different FPGA fabrics. DeepMaker enables effective use of DNN acceleration in commercially available devices that can accelerate a wide range of applications without a need of costly FPGA reconﬁgurations.
Energy-Efficient Hardware Accelerator for Embedded Deep Learning
In this joint project, we aim at decreasing the power consumption and computation load of the current image processing platform by employing the concept of computation reuse. Computation reuse suggests temporarily storing and reusing the result of a recent arithmetic operation for anticipated subsequent operations with the same operands. Our proposal is motivated by the high degree of redundancy that we observed in arithmetic operations of neural networks where we show that an approximate computation reuse can eliminate up to 94% of arithmetic operation of simple neural networks. This leads to up to 80% reduction in power consumption, which directly translates to a considerable increase in battery life time. We further presented a mechanism to make a large neural network by connecting basic units in two UT-MDH joint works.