System Identification I

Deep learning inference is increasingly run at the edge. As the programming and system stack support becomes mature, it enables acceleration opportunities in a mobile system, where the system performance envelope is scaled up with a plethora of programmable co-processors. Thus, intelligent services designed for mobile users can choose between running inference on the CPU or any of the co-processors in the mobile system, and exploiting connected systems such as the cloud or a nearby, locally connected mobile system. By doing so, these services can scale out the performance and increase the energy efficiency of edge mobile systems. This gives rise to a new challenge—deciding when inference should run where. Such execution scaling decision becomes more complicated with the stochastic nature of mobile-cloud execution environment, where signal strength variation in the wireless networks and resource interference can affect real-time inference performance and system energy efficiency. To enable energy efficient deep learning inference at the edge, this paper proposes AutoScale, an adaptive and lightweight execution scaling engine built on the custom-designed reinforcement learning algorithm. It continuously learns and selects the most energy efficient inference execution target by considering characteristics of neural networks and available systems in the collaborative cloud-edge execution environment while adapting to stochastic runtime variance. Real system implementation and evaluation, considering realistic execution scenarios, demonstrate an average of 9.8x and 1.6x energy efficiency improvement over the baseline mobile CPU and cloud offloading, respectively, while meeting the real-time performance and accuracy requirements.

AutoScale: Energy Efficiency Optimization for Stochastic Edge Inference Using Reinforcement Learning

Real-time carbon emission monitoring in prefabricated construction

The cyber-physical system (CPS) has operated, controlled, and coordinated the physical systems integrated by a computing and communication core applied in Industry 4.0. To accommodate CPS services, fog radio and optical networks (F-RON) has become an important supporting physical cyber infrastructure taking advantage of both the inherent ubiquity of wireless technology and the large capacity of optical networks. However, cyber security is the biggest issue in the CPS scenario as there is a trade-off between security control and privacy exposure in F-RON. To deal with this issue, we propose a brain-like distributed control security (BLCS) architecture for F-RON in CPS by introducing a brain-like security (BLS) scheme. BLCS can accomplish the secure cross-domain control among tripartite controllers verification in the scenario of decentralized F-RON for distributed computing and communications, which has no need to disclose the private information of each domain against cyber-attacks. BLS utilizes parts of information to perform control identification through a relation network and a deep learning of behavior library. The functional modules of BLCS architecture are illustrated including various controllers and a brain-like knowledge base. The interworking procedures in distributed control security modes based on BLS are described. The overall feasibility and efficiency of the architecture are experimentally verified on a software defined network testbed in terms of average mistrust rate, path provisioning latency, packet loss probability, and blocking probability. The emulation results are obtained and dissected based on the testbed.

BLCS: Brain-Like Distributed Control Security in Cyber Physical Systems

Emerging architecture for heterogeneous smart cyber-physical systems for industry 5.0

Resource management strategies for many-core systems dictate the sharing of resources among applications such as power, processing cores, and memory bandwidth in order to achieve system goals. System goals require consideration of both system constraints (e.g., power envelope) and user demands (e.g., response time, energy-efficiency). Existing approaches use heuristics, control theory, and machine learning for resource management. They all depend on static system models, requiring a priori knowledge of system dynamics, and are therefore too rigid to adapt to emerging workloads or changing system dynamics. We present SOSA, a cross-layer hardware/software hierarchical resource manager. Low-level controllers optimize knob configurations to meet potentially conflicting objectives (e.g., maximize throughput and minimize energy). SOSA accomplishes this for many-core systems and unpredictable dynamic workloads by using rule-based reinforcement learning to build subsystem models from scratch at runtime. SOSA employs a high-level supervisor to respond to changing system goals due to operating condition, e.g., switch from maximizing performance to minimizing power due to a thermal event. SOSA's supervisor translates the system goal into low-level objectives (e.g., core instructions-per-second (IPS)) in order to control subsystems by coordinating numerous knobs (e.g., core operating frequency, task distribution) towards achieving the goal. The software supervisor allows for flexibility, while the hardware learners allow quick and efficient optimization. We evaluate a simulation-based implementation of SOSA and demonstrate SOSA's ability to manage multiple interacting resources in the presence of conflicting objectives, its efficiency in configuring knobs, and adaptability in the face of unpredictable workloads. Executing a combination of machine-learning kernels and microbenchmarks on a multicore system-on-a-chip, SOSA achieves target performance with less than 1% error starting with an untrained model, maintains the performance in the face of workload disturbance, and automatically adapts to changing constraints at runtime. We also demonstrate the resource manager with a hardware implementation on an FPGA.

https://dl.acm.org/doi/pdf/10.1145/3352460.3358312

SOSA: Self-Optimizing Learning with Self-Adaptive Control for Hierarchical System-on-Chip Management

Future cyber–physical systems will host a large number of coexisting distributed applications on hardware platforms with thousands to millions of networked components communicating over open networks. These applications and networks are subject to continuous change. The current separation of design process and operation in the field will be superseded by a life-long design process of adaptation, infield integration, and update. Continuous change and evolution, application interference, environment dynamics and uncertainty lead to complex effects which must be controlled to serve a growing set of platform and application needs. Self-adaptation based on self-awareness and self-configuration has been proposed as a basis for such a continuous in-field process. Research is needed to develop automated in-field design methods and tools with the required safety, availability, and security guarantees. The paper shows two complementary use cases of self-awareness in architectures, methods, and tools for cyber–physical systems. The first use case focuses on safety and availability guarantees in self-aware vehicle platforms. It combines contracting mechanisms, tool based self-analysis and self-configuration. A software architecture and a runtime environment executing these tools and mechanisms autonomously are presented including aspects of self-protection against failures and security threats. The second use case addresses variability and long term evolution in networked MPSoC integrating hardware and software mechanisms of surveillance, monitoring, and continuous adaptation. The approach resembles the logistics and operation principles of manufacturing plants which gave rise to the metaphoric term of an Information Processing Factory that relies on incremental changes and feedback control. Both use cases are investigated by larger research groups. Despite their different approaches, both use cases face similar design and design automation challenges which will be summarized in the end. We will argue that seemingly unrelated research challenges, such as in machine learning and security, could also profit from the methods and superior modeling capabilities of self-aware systems.

/pdf/platform-centric-self-awareness-as-a-key-enabler-for-3l5hdjtrzb.pdf

Platform-Centric Self-Awareness as a Key Enabler for Controlling Changes in CPS

This paper deals with challenges and possible solutions for incorporating self-awareness principles in EDA design flows for autonomous systems. We present a holistic approach that enables self-awareness across the software/hardware stack, from systems-on-chip to systems-of-systems (autonomous car) contexts. We use the Information Processing Factory (IPF) metaphor as an exemplar to show how self-awareness can be achieved across multiple abstraction levels, and discuss new research challenges. The IPF approach represents a paradigm shift in platform design by envisioning the move towards a consequent platform-centric design in which the combination of self-organizing learning and formal reactive methods guarantee the applicability of such cyber-physical systems in safety-critical and high-availability applications.

Armin Sadighi

Papers

SOSA: Self-Optimizing Learning with Self-Adaptive Control for Hierarchical System-on-Chip Management

Platform-Centric Self-Awareness as a Key Enabler for Controlling Changes in CPS

Design methodologies for enabling self-awareness in autonomous systems