TY - THES
AU - Witschen, Linus Matthias
ID - 34041
TI - Frameworks and Methodologies for Search-based Approximate Logic Synthesis
ER -
TY - CONF
AU - Ahmed, Qazi Arbab
AU - Platzner, Marco
ID - 32342
TI - On the Detection and Circumvention of Bitstream-Level Trojans in FPGAs
ER -
TY - GEN
AU - Mehlich, Florian
ID - 42839
TI - An Evaluation of XCS on the OpenAI Gym
ER -
TY - JOUR
AB - Deep neural networks (DNNs) are penetrating into a broad spectrum of applications and replacing manual algorithmic implementations, including the radio frequency communications domain with classical signal processing algorithms. However, the high throughput (gigasamples per second) and low latency requirements of this application domain pose a significant hurdle for adopting computationally demanding DNNs. In this article, we explore highly specialized DNN inference accelerator approaches on field-programmable gate arrays (FPGAs) for RadioML modulation classification. Using an automated end-to-end flow for the generation of the FPGA solution, we can easily explore a spectrum of solutions that optimize for different design targets, including accuracy, power efficiency, resources, throughput, and latency. By leveraging reduced precision arithmetic and customized streaming dataflow, we demonstrate a solution that meets the application requirements and outperforms alternative FPGA efforts by 3.5x in terms of throughput. Against modern embedded graphics processing units (GPUs), we measure >10x higher throughput and >100x lower latency under comparable accuracy and power envelopes.
AU - Jentzsch, Felix
AU - Umuroglu, Yaman
AU - Pappalardo, Alessandro
AU - Blott, Michaela
AU - Platzner, Marco
ID - 33990
IS - 6
JF - IEEE Micro
TI - RadioML Meets FINN: Enabling Future RF Applications With FPGA Streaming Architectures
VL - 42
ER -
TY - GEN
AU - Tcheussi Ngayap, Vanessa Ingrid
ID - 45715
TI - FreeRTOS on a MicroBlaze Soft-Core Processor with Hardware Accelerators
ER -
TY - GEN
AU - Manjunatha, Suraj
ID - 45914
TI - Dealing With Pre-Processing And Feature Extraction Of Time-Series Data In Predictive Maintenance
ER -
TY - GEN
AU - Kaur , Parvinder
ID - 45915
TI - Analysis of Time-Series Classification in Conditional Monitoring Systems
ER -
TY - THES
AB - Previous research in proof-carrying hardware has established the feasibility and utility of the approach, and provided a concrete solution for employing it for the certification of functional equivalence checking against a specification, but fell short in connecting it to state-of-the-art formal verification insights, methods and tools. Due to the immense complexity of modern circuits, and verification challenges such as the state explosion problem for sequential circuits, this restriction of readily-available verification solutions severely limited the applicability of the approach in wider contexts.
This thesis closes the gap between the PCH approach and current advances in formal hardware verification, provides methods and tools to express and certify a wide range of circuit properties, both functional and non-functional, and presents for the first time prototypes in which circuits that are implemented on actual reconfigurable hardware are verified with PCH methods. Using these results, designers can now apply PCH to establish trust in more complex circuits, by using more diverse properties which they can express using modern, efficient property specification techniques.
AU - Wiersema, Tobias
ID - 26746
KW - Proof-Carrying Hardware
KW - Formal Verification
KW - Sequential Circuits
KW - Non-Functional Properties
KW - Functional Properties
TI - Guaranteeing Properties of Reconfigurable Hardware Circuits with Proof-Carrying Hardware
ER -
TY - JOUR
AB - Robotics applications process large amounts of data in real time and require compute platforms that provide high performance and energy efficiency. FPGAs are well suited for many of these applications, but there is a reluctance in the robotics community to use hardware acceleration due to increased design complexity and a lack of consistent programming models across the software/hardware boundary. In this article, we present ReconROS, a framework that integrates the widely used robot operating system (ROS) with ReconOS, which features multithreaded programming of hardware and software threads for reconfigurable computers. This unique combination gives ROS 2 developers the flexibility to transparently accelerate parts of their robotics applications in hardware. We elaborate on the architecture and the design flow for ReconROS and report on a set of experiments that underline the feasibility and flexibility of our approach.
AU - Lienen, Christian
AU - Platzner, Marco
ID - 29150
JF - ACM Transactions on Reconfigurable Technology and Systems
SN - 1936-7406
TI - Design of Distributed Reconfigurable Robotics Systems with ReconROS
ER -
TY - GEN
AB - Automation becomes a vital part in the High-Performance computing system in situational dynamics to take the decisions on the fly. Heterogeneous compute nodes consist of computing resources such as CPU, GPU and FPGA and are the important components of the high-performance computing system that can adapt the automation to achieve the given goal. While implanting automation in the computing resources, management of the resources is one of the essential aspects that need to be taken care of. Tasks are continuously executed on the resources using its unique characteristics. Effective scheduling is essential to make the best use of the characteristics provided by each resource. Scheduling enables the execution of each task by allocating resources so that they take advantage of all the characteristics of the compute resources. Various scheduling heuristics can be used to create effective scheduling, which might require the execution time to schedule the task efficiently. Providing actual execution time is not possible in many cases; hence we can provide the estimations for the actual execution time . The purpose of this master's thesis is to design a predictive model or system that estimates the execution time required to execute tasks using historical execution time data on the heterogeneous compute nodes. In this thesis, regression techniques(SGD Regressor, Passive-Aggressive Regressor, MLP Regressor, and XCSF Regressor) are compared in terms of their prediction accuracy in order to determine which technique produces reliable predictions for the execution time. These estimations must be generated in an online learning environment in which data points arrive in any sequence, one by one, and the regression model must learn from them. After evaluating the regression algorithms, it is seen that the XCSF regressor provides the highest overall prediction accuracy for the supplied data sets. The regression technique's parameters also play a significant role in achieving an acceptable prediction accuracy. As a remark, when using online learning in regression analysis, the accuracy depends upon both the order of sequential data points that are coming to train the model and the parameter configuration for each regression technique.
AU - Kashikar, Chinmay
ID - 29151
TI - A Comparison of Machine Learning Techniques for the On-line Characterization of Tasks Executed on Heterogeneous Compute Nodes
ER -
TY - CONF
AU - Awais, Muhammad
AU - Ghasemzadeh Mohammadi, Hassan
AU - Platzner, Marco
ID - 21610
T2 - Proceedings of the ACM Great Lakes Symposium on VLSI (GLSVLSI) 2021
TI - LDAX: A Learning-based Fast Design Space Exploration Framework for Approximate Circuit Synthesis
ER -
TY - GEN
AU - Rehnen, Jakob Werner
ID - 22216
TI - Decomposition of Arithmetic Components for the Approximate Circuit Synthesis with EvoApproxLib
ER -
TY - CONF
AB - Approximate computing (AC) has acquired significant maturity in recent years as a promising approach to obtain energy and area-efficient hardware. Automated approximate accelerator synthesis involves a great deal of complexity on the size of design space which exponentially grows with the number of possible approximations. Design space exploration of approximate accelerator synthesis is usually targeted via heuristic-based search methods. The majority of existing frameworks prune a large part of the design space using a greedy-based approach to keep the problem tractable. Therefore, they result in inferior solutions since many potential solutions are neglected in the pruning process without the possibility of backtracking of removed approximate instances. In this paper, we address the aforementioned issue by adopting Monte Carlo Tree Search (MCTS), as an efficient stochastic learning-based search algorithm, in the context of automated synthesis of approximate accelerators. This enables the synthesis frameworks to deeply subsamples the design space of approximate accelerator synthesis toward most promising approximate instances based on the required performance goals, i.e., power consumption, area, or/and delay. We investigated the challenges of providing an efficient open-source framework that benefits analytical and search-based approximation techniques simultaneously to both speed up the synthesis runtime and improve the quality of obtained results. Besides, we studied the utilization of machine learning algorithms to improve the performance of several critical steps, i.e., accelerator quality testing, in the synthesis framework. The proposed framework can help the community to rapidly generate efficient approximate accelerators in a reasonable runtime.
AU - Awais, Muhammad
AU - Platzner, Marco
ID - 22309
KW - Approximate computing
KW - Design space exploration
KW - Accelerator synthesis
T2 - Proceedings of IEEE Computer Society Annual Symposium on VLSI
TI - MCTS-Based Synthesis Towards Efficient Approximate Accelerators
ER -
TY - GEN
AB - This bachelor thesis presents a C/C++ implementation of the XCS algorithm for an embedded system and profiling results concerning the execution time of the functions. These are then analyzed in relation to the input characteristics of the examined learning environments and compared with related work. Three main conclusions can be drawn from the measured results. First, the maximum size of the population of the classifiers influences the runtime of the genetic algorithm; second, the size of the input space has a direct effect on the execution time of the matching function; and last, a larger action space results in a longer runtime generating the prediction for the possible actions. The dependencies identified here can serve to optimize the computational efficiency and make XCS more suitable for embedded systems.
AU - Brede, Mathis
ID - 22483
TI - Implementation and Profiling of XCS in the Context of Embedded Systems
ER -
TY - CONF
AU - Witschen, Linus Matthias
AU - Wiersema, Tobias
AU - Raeisi Nafchi, Masood
AU - Bockhorn, Arne
AU - Platzner, Marco
ED - Hannig, Frank
ED - Derrien, Steven
ED - Diniz, Pedro
ED - Chillet, Daniel
ID - 21953
T2 - Proceedings of International Symposium on Applied Reconfigurable Computing (ARC'21)
TI - Timing Optimization for Virtual FPGA Configurations
ER -
TY - JOUR
AB - Abstract
Background
Hand amputation can have a truly debilitating impact on the life of the affected person. A multifunctional myoelectric prosthesis controlled using pattern classification can be used to restore some of the lost motor abilities. However, learning to control an advanced prosthesis can be a challenging task, but virtual and augmented reality (AR) provide means to create an engaging and motivating training.
Methods
In this study, we present a novel training framework that integrates virtual elements within a real scene (AR) while allowing the view from the first-person perspective. The framework was evaluated in 13 able-bodied subjects and a limb-deficient person divided into intervention (IG) and control (CG) groups. The IG received training by performing simulated clothespin task and both groups conducted a pre- and posttest with a real prosthesis. When training with the AR, the subjects received visual feedback on the generated grasping force. The main outcome measure was the number of pins that were successfully transferred within 20 min (task duration), while the number of dropped and broken pins were also registered. The participants were asked to score the difficulty of the real task (posttest), fun-factor and motivation, as well as the utility of the feedback.
Results
The performance (median/interquartile range) consistently increased during the training sessions (4/3 to 22/4). While the results were similar for the two groups in the pretest, the performance improved in the posttest only in IG. In addition, the subjects in IG transferred significantly more pins (28/10.5 versus 14.5/11), and dropped (1/2.5 versus 3.5/2) and broke (5/3.8 versus 14.5/9) significantly fewer pins in the posttest compared to CG. The participants in IG assigned (mean ± std) significantly lower scores to the difficulty compared to CG (5.2 ± 1.9 versus 7.1 ± 0.9), and they highly rated the fun factor (8.7 ± 1.3) and usefulness of feedback (8.5 ± 1.7).
Conclusion
The results demonstrated that the proposed AR system allows for the transfer of skills from the simulated to the real task while providing a positive user experience. The present study demonstrates the effectiveness and flexibility of the proposed AR framework. Importantly, the developed system is open source and available for download and further development.
AU - Boschmann, Alexander
AU - Neuhaus, Dorothee
AU - Vogt, Sarah
AU - Kaltschmidt, Christian
AU - Platzner, Marco
AU - Dosen, Strahinja
ID - 30906
IS - 1
JF - Journal of NeuroEngineering and Rehabilitation
KW - Health Informatics
KW - Rehabilitation
SN - 1743-0003
TI - Immersive augmented reality system for the training of pattern classification control with a myoelectric prosthesis
VL - 18
ER -
TY - JOUR
AU - Rodriguez, Alfonso
AU - Otero, Andres
AU - Platzner, Marco
AU - De la Torre, Eduardo
ID - 30907
JF - IEEE Transactions on Computers
KW - Computational Theory and Mathematics
KW - Hardware and Architecture
KW - Theoretical Computer Science
KW - Software
SN - 0018-9340
TI - Exploiting Hardware-Based Data-Parallel and Multithreading Models for Smart Edge Computing in Reconfigurable FPGAs
ER -
TY - CONF
AU - Hansmeier, Tim
ID - 29137
T2 - HEART '21: Proceedings of the 11th International Symposium on Highly Efficient Accelerators and Reconfigurable Technologies
TI - Self-aware Operation of Heterogeneous Compute Nodes using the Learning Classifier System XCS
ER -
TY - GEN
AB - Autonomous mobile robots are becoming increasingly more capable and widespread. Reliable Obstacle avoidance is an integral part of autonomous navigation. This involves real time interpretation and processing of a complex environment. Strict time and energy constraints of a mobile autonomous system make efficient computation extremely desirable. The benefits of employing Hardware/Software co-designed applications are obvious and significant. Hardware accelerators are used for efficient processing of the algorithms by exploiting parallelism. FPGAs are a class of hardware accelerators, which
can contain hundreds of small execution units, and can be used for Hardware/Software co-designed application. However, there is a reluctance when it comes to adoption of these devices in well established application domains, such as Robotics, due to a steep learning curve needed for FPGA application design. ReconROS has successfully bridged the gap between robotic and FPGA application development, by providing an intuitive, common development platform for robotic application development for FPGA. It does so by integrating Robotics Operating System(ROS) which is an industry and academia standard for robotics application development, with ReconOS, an operating system for re-configurable hardware. In this thesis an obstacle avoidance system is designed and implemented for an autonomous vehicle using ReconROS. The objectives of the thesis is to demonstrate and explore ReconROS integration within the ROS ecosystem and explore the design process within ReconROS framework, and to demonstrate the effectiveness of Hardware Acceleration in Robotics, by analysing the resulting architectures for Latency and Power Consumption.
AU - Sheikh, Muhammad Aamir
ID - 29540
TI - Design and Implementation of a ReconROS-based Obstacle Avoidance System
ER -
TY - GEN
AB - Robotics applications process large amounts of data in real-time and require compute platforms that provide high performance and energy-efficiency. FPGAs are well-suited for many of these applications, but there is a reluctance in the robotics community to use hardware acceleration due to increased design complexity and a lack of consistent programming models across the software/hardware boundary. In this paper we present ReconROS, a framework that integrates the widely-used robot operating system (ROS) with ReconOS, which features multithreaded programming of hardware and software threads for reconfigurable computers. This unique combination gives ROS2 developers the flexibility to transparently accelerate parts of their robotics applications in hardware. We elaborate on the architecture and the design flow for ReconROS and report on a set of experiments that underline the feasibility and flexibility of our approach.
AU - Lienen, Christian
AU - Platzner, Marco
ID - 22764
T2 - arXiv:2107.07208
TI - Design of Distributed Reconfigurable Robotics Systems with ReconROS
ER -