Axel Jantsch

• KTH Royal Institute of Technology

This survey provides an overview of the state-of-the-art in runtime adaptive Approximate Computing (AxC) for Deep Learning (DL) algorithms, highlighting the challenges and opportunities in the field. The survey covers a broad spectrum of applications, including medical applications, computer vision, and natural language processing. Various power-co...

Connecting smart industrial components to computer networks revolutionizes business operations. However, in the Industrial Internet of Things (IIoT), the sharing of data has bandwidth, computational, and privacy issues. Researchers presented cloud computing and fine-grained access control to overcome these challenges. However, traditional centraliz...

Today, there exists a large number of different embedded hardware platforms for accelerating the inference of Deep Neural Networks (DNNs). To enable rapid application development, a number of prediction frameworks have been proposed to estimate the DNN inference latency on a wide range of hardware platforms. This work presents a novel smart padding...

In condition monitoring, early detection of process signal drifts indicating, e.g., equipment degradation is crucial. exponentially weighted moving average (EWMA), cumulative sum (CUSUM), and discrete average block (DAB)-based drift detectors are statistical and commonly used methods. Each has benefits and limitations, suited to different data type...

In this paper, we introduce a novel technique called EMAC (E-Multiplication and Accumulation (MAC)), aimed at enhancing energy efficiency, reducing latency, and improving the accuracy of analog-based in-Static Random Access Memory (SRAM) MAC accelerators. Our approach involves a digital-to-time Word-Line (WL) modulation technique that encodes the W...

A pedestrian detection system in a traffic light controller is studied. The system is based on Deep Neural Networks (DNNs). We explore several network architectures and hardware platforms to identify the most suitable solution under the given constraints of latency, cost, and precision. Specifically, we study altogether 13 networks from the MobileN...

Ambrosia artemisiifolia, also known as ragweed, is an invasive weed species that aggressively spreads across Europe. 4–5% of the population suffers from strong allergic reactions to its pollen. This work studies the use of aerial drones equipped with highly compressed deep neural networks (DNNs) to scan large areas of vegetation for ragweed with hi...

Cite as: Author/s, “Title of contribution-presentation”, in Proceedings of the 3rd Summer School on Cyber-Physical Systems and Internet-of Things, Editors: Lech Jozwiak, Radovan Stojanovic and Nikolaos Voros, Vol. III, June 2022, pp. xx-yy, DOI: https://doi.org/10.5281/zenodo.6698644 Citation example: Lech Jóźwiak, Green CPS and IoT for Green Wo...

We present a cognitive mobile robot that acquires knowledge, and autonomously learns higher-level abstract capabilities based on play instincts, inspired by human behavior. To this end, we (i) model skills, (ii) model the robot’s sensor and actuator space based on elementary physical properties, and (iii) propose algorithms inspired by humans’ play...

Cardiovascular diseases are one of the world's major causes of loss of life. The vital signs of a patient can indicate this up to 24 hours before such an incident happens. Healthcare professionals use Early Warning Score (EWS) as a common tool in healthcare facilities to indicate the health status of a patient. However , the chance of survival of a...

Image processing systems exploit image information for a purpose determined by the application at hand. The implementation of image processing systems in an Internet of Things (IoT) context is a challenge due to the amount of data in an image processing system, which affects the three main node constraints: memory, latency and energy. One method to...

Employing convolutional neural network models for large scale datasets represents a big challenge. Especially embedded devices with limited resources cannot run most state-of-the-art model architectures in real-time, necessary for many applications. This paper proves the applicability of shunt connections on large scale datasets and narrows this co...

Today's energy grids face an increasing number of decentralized and renewable energy sources as well as growing e-mobility. Therefore, reliable grid monitoring becomes a key element for a sustainable grid operation. Traditional grid monitoring concepts are either fully manual, need a detailed system model, or rely on computationally heavy machine l...

With new accelerator hardware for DNN, the computing power for AI applications has increased rapidly. However, as DNN algorithms become more complex and optimized for specific applications, latency requirements remain challenging, and it is critical to find the optimal points in the design space. To decouple the architectural search from the target...

Optimising the energy consumption of IoT nodes can be tedious due to the due to complex trade-offs involved between processing and communication. In this article, we investigate the partitioning of processing between the sensor node and a server and study the energy trade-offs involved. We propose a method that provides a trade-off analysis for a g...

Teaching and learning design, test, and prototyping of digital hardware requires substantial resources from both students (tool-chains, licenses, and FPGA development kits) and universities (laboratories, licenses, and substantial human resources). Mass E-Learning Of design, test, and prototyping DIgital hardware (MELODI) provides an efficient and...

With more powerful yet efficient embedded devices and accelerators being available for Deep Neural Networks (DNN), machine learning is becoming an integral part of edge computing. As the number of such devices increases, finding the best platform for a specific application has become more challenging. A common question for application developers is...

With new accelerator hardware for Deep Neural Networks (DNNs), the computing power for Artificial Intelligence (AI) applications has increased rapidly. However, as DNN algorithms become more complex and optimized for specific applications, latency requirements remain challenging, and it is critical to find the optimal points in the design space. To...

Embedded systems have proliferated in various consumer and industrial applications with the evolution of Cyber-Physical Systems and the Internet of Things. These systems are subjected to stringent constraints so that embedded software must be optimized for multiple objectives simultaneously, namely reduced energy consumption, execution time, and co...

italic xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">Editor’s notes: Trustworthiness is key for the acceptance of autonomous systems. The authors advocate deterministic methods with hard-bounded corridors for operational parameters to guarantee dependable autonomy considering both functional and extra-fu...

Runtime resource management for many-core systems is increasingly complex. The complexity can be due to diverse workload characteristics with conflicting demands, or limited shared resources such as memory bandwidth and power. Resource management strategies for many-core systems must distribute shared resource(s) appropriately across workloads, whi...

In smart manufacturing, the demand increases to be able to monitor and adjust process execution during production. It has led to a shift towards distributed, modular automation. A solution seems to be to use a cognitive architecture. It turns out that their generality often makes them unsuitable for specific industrial problems. In this paper, we p...

The role of smart and autonomous systems is becoming vital in many areas of industry and society. Expectations from such systems continuously rise and become more ambitious: long lifetime, high reliability, high performance, energy efficiency, and adaptability, particularly in the presence of changing environments. Computational self-awareness prom...

In this paper we make the case for the new class of Self-aware Cyber-physical Systems. By bringing together the two established fields of cyber-physical systems and self-aware computing, we aim at creating systems with strongly increased yet managed autonomy, which is a main requirement for many emerging and future applications and technologies. Se...

Embodied self-aware computing systems are embedded in a physical environment with a rich set of sensors and actuators to interact both with their environment and with their own embodiment. Through this interaction, they learn about their situation, their own state, and their performance. Although they are application specific like traditional embed...

In recent years, interest in self-learning methods has increased significantly. A challenge is to learn to survive in a real or simulated world by solving tasks with as little prior knowledge about itself, the task, and the environment. In this paper, the state-of-the-art methods of reinforcement learning, in particular, Q-learning, are analyzed re...

The overlap of the two established fields of cyber-physical systems and self-aware computing systems constitutes a challenging class of systems that require autonomy and must satisfy multiple, possibly conflicting constraints (e.g., performance, timeliness, energy, reliability). Self-aware cyber-physical systems are situated in dynamic physical env...

In the era of Fog computing where one can decide to compute certain time-critical tasks at the edge of the network, designers often encounter a question whether the sensor layer provides the optimal response time for a service, or the Fog layer, or their combination. In this context, minimizing the total response time using computation migration is...

We define quality of service requirements for mixed-criticality systems based on min-plus algebra rather than discrete criticality levels. The requirements (1) unify a spectrum of weakly-hard real-time requirements with strongly-hard real-time and soft real-time as extreme cases and (2) support dynamic tuning of task importance. The paper elaborate...

Management of energy dissipation and battery life is a challenge in health monitoring wearables. Low-quality data collection, non-reliable monitoring process, and missing important health events are consequences of single-goal fixed-policy solutions. In this research, energy dissipation of IoT-based wearable systems is managed through a dynamic mul...

Signals obtained from a patient i.e., bio-signals are utilized to analyze the health of patient. One such bio-signal of paramount importance is the Electrocardiogram (ECG), represents the functioning of the heart. Any abnormal behavior in the ECG signal is an indicative measure of malfunctioning of the heart termed as arrhythmia condition. Due to t...

Run-time resource allocation of heterogeneous multi-core systems is challenging with varying workloads and limited power and energy budgets. User interaction within these systems changes the performance requirements, often conflicting with concurrent applications' objective and system constraints. Current resource allocation approaches focus on opt...

Computational Self-awareness can improve performance, robustness, and adaptivity of a system. As a key element of self-awareness, observation quality is critical to gain a correct and comprehensive understanding of the system, its own state, and the environment. This is of more importance in systems, where contextual information play a crucial role...

Management of energy dissipation and battery life is a challenge in health monitoring wearables. Low-quality data collection, non-reliable monitoring process, and missing important health events are consequences of single-goal fixed-policy solutions. In this research, energy dissipation of IoT-based wearable systems is managed through a dynamic mul...

Resource management has a long history in computing, from the early days of time-shared machines with pioneering fundamental work on run-time systems, distributed systems, real-time operating systems and middleware. The longevity and fundamental importance of the topic has resulted in an incredibly large body of work for on-chip resource management...

bold xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">Complex SoCs require sophisticated and dynamic management of resources because there are so many competing and equally important objectives to be pursued. This is the second in a series of two special issues on self-aware SoCs. Figure 1 illustrates the...

To achieve high reliability in on-chip networks, it is necessary to test the network continuously with Built-in Self-Tests (BIST) so that the faults can be detected quickly and the number of affected packets can be minimized. However, BIST causes significant performance loss due to data dependencies. We propose EsyTest, a comprehensive test strateg...

Deployment of Deep Neural Networks (DNNs) on hardware platforms is often constrained by limited on-chip memory and computational power. The proposed weight quantization offers the possibility of optimizing weight memory alongside transforming the weights to hardware friendly data-types. We apply Dynamic Fixed Point and Power-of-two quantization in...

Due to the high integration density and roadblock of voltage scaling, modern multi-core processors experience higher power densities than previous technology scaling nodes. When unattended, this issue might lead to temperature hot spots, that in turn may cause non-uniform aging, accelerate chip failure, impair reliability, and reduce the performanc...

Factories in Industry 4.0 are growing in complexity due to the incorporation of a large number of Cyber-Physical System (CPSs) which are logically and often physically distributed. Traditional monolithic control and monitoring structures are not able to address the increasing requirements regarding flexibility, operational time, and efficiency as w...

Presented in the Work in Progress Session of IEEE/Euromicro International Conference on Digital System Design 2018.

Anomaly detection in Electrocardiogram (ECG) signals facilitates the diagnosis of cardiovascular diseases i.e., arrhythmias. Existing methods, although fairly accurate, demand a large number of computational resources. Based on the pre-processing of ECG signal, we present a low-complex digital hardware implementation (ADDHard) for arrhythmia detect...

In real-time health analytics, smart cities, military sensing systems, and others, big data analytics is enabled by the introduction of appropriate sensing and actuation systems. The introduction of next generation of sensing and actuation systems or the Internet of Things era has been facilitated by affordable low-power 32-bit microcontrollers com...

Resource management strategies for many-core systems need to enable sharing of resources such as power, processing cores, and memory bandwidth while coordinating the priority and significance of system- and application-level objectives at runtime in a scalable and robust manner. State-of-the-art approaches use heuristics or machine learning for res...

Resource management strategies for many-core systems need to enable sharing of resources such as power, processing cores, and memory bandwidth while coordinating the priority and significance of system- and application-level objectives at runtime in a scalable and robust manner. State-of-the-art approaches use heuristics or machine learning for res...

Heterogeneous multi-core processors (HMPs) are increasingly being deployed to meet the performance/power requirements of emerging workloads, demanding adaptive and coordinated resource management techniques to control the resulting complexity of such systems. While multiple input, multiple output (MIMO) control-theory has been applied to adaptively...

Limitation on power budget in many-core systems leaves a fraction of on-chip resources inactive, referred to as dark silicon. An efficient run-time system that manages critical interlinked parameters of power, performance and temperature can enhance resource utilization and mitigate dark silicon. In this paper, we present a run-time resource manage...

The core insight that motivates research on self-aware SoCs is that good decision making requires a sound understanding of the situation. As there are many decisions to be taken for the resource and infrastructure management of complex, heterogeneous SoCs, the collection of data about the chip and its environment, and their interpretation is well j...

Many-core systems are highly complex and require thorough orchestration of different goals across the computing abstraction stack to satisfy embedded system constraints. Contemporary resource management approaches typically focus on a fixed objective, while neglecting the need for replanning (i.e., updating the objective function). This trend is pa...

italic xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">Editor’s note: Self-awareness is a desirable feature of emerging computing systems. It helps systems to understand, manage, and report on their own system behavior. This paper presents an overview centered around the paradigm of self-awareness in compu...

To highlight a potential threat to hardware security, we propose a methodology to derive a trigger signal from the behavior of Verilog simulation models of field-programmable gate array (FPGA) primitives that behave X-optimistic. We demonstrate our methodology with an example trigger that is implemented using Xilinx 7 Series FPGAs. Experimental res...

Cyber-Physical Systems (CPS) pose new challenges to verification and validation that go beyond the proof of functional correctness based on high-level models. Particular challenges are, in particular for formal methods, its heterogeneity and scalability. For numerical simulation, uncertain behavior can hardly be covered in a comprehensive way which...

Cyber-Physical Systems (CPS) pose new challenges to verification and validation that go beyond the proof of functional correctness based on high-level models. Particular challenges are, in particular for formal methods, its heterogeneity and scalability. For numerical simulation, uncertain behavior can hardly be covered in a comprehensive way which...

This book describes state-of-the-art approaches to Fog Computing, including the background of innovations achieved in recent years. Coverage includes various aspects of fog computing architectures for Internet of Things, driving reasons, variations and case studies. The authors discuss in detail key topics, such as meeting low latency and real-time...

The functional separation of different system components has been used to address some critical challenges in architecture design. One of the well-known approaches of physical separation of functional units is in client–server architectures. The server side of this separation is hidden inside the cloud infrastructure in the case of an Internet scal...

To tolerate faults in Networks-on-Chip (NoC), routers are often disconnected from the NoC, which affects the system integrity. This is because cores connected to the disabled routers cannot be accessed from the network, resulting in loss of function and performance. We propose E-Rescuer, a technique offering a reconfigurable router architecture and...

Power capping techniques based on dynamic voltage and frequency scaling (DVFS) and power gating (PG) are oriented toward power actuation, compromising on performance and energy. Inherent error resilience of emerging application domains, such as Internet-of-Things (IoT) and machine learning, provides opportunities for energy and performance gains. L...

Summarized under the term Transport-by-Throwing, robotic arms throwing objects to each other are a visionary system intended to complement the conventional, static conveyor belt. Despite much research and many novel approaches, no fully satisfactory solution to catch a ball with a robotic arm has been developed so far. A new approach based on memor...

New power management challenges in networked many-core systems arise when limitations of the dark silicon era come into reality. The main goal in the power management process is to achieve optimal power-performance efficiency considering thermal design power (TDP) budget. This necessitates: (1) monitoring several system characteristics including bo...

An efficient run-time application mapping approach can considerably enhance resource utilization and mitigate the dark silicon phenomenon. In this chapter, we present a dark silicon aware run-time application mapping approach that patterns active cores alongside the inactive cores in order to evenly distribute power density across the chip. This ap...

The possibilities to increase single-core performance have ended due to limited instruction-level parallelism and a high penalty when increasing frequency. This prompted designers to move toward multi-core paradigms [1], largely supported by transistor scaling [2]. Scaling down transistor gate length makes it possible to switch them faster at a low...

We present a novel type of Trojan trigger targeted at the field-programmable gate array (FPGA) design flow. Traditional triggers base on rare events, such as rare values or sequences. While in most cases these trigger circuits are able to hide a Trojan attack, exhaustive functional simulation and testing will reveal the Trojan due to violation of t...

Early Warning Score (EWS) systems are utilized in hospitals by health-care professionals to interpret vital signals of patients. These scores are used to measure and predict amelioration or deterioration of patients’ health status to intervene in an appropriate manner when needed. Based on an earlier work presenting an automated Internet-of-Things...

Power Capping techniques are used to restrict power consumption of computer systems to a thermally safe limit. Current many-core systems employ dynamic voltage and frequency scaling (DVFS), power gating (PG) and scheduling methods as actuators for power capping. These knobs are oriented towards power actuation, while the need for performance and en...

The communication in Network-on-Chips (NoCs) may be subject to errors. Error Correcting Codes (ECCs) can be used to tolerate the transient faults in flits caused by Single Event Upsets (SEU). ECC can improve the reliability of a NoC significantly at the cost of extra area and power consumption. However, ECC units (encoders and decoders) may also su...