Refine
Document Type
- Conference proceeding (4)
- Book chapter (1)
Language
- English (5)
Has full text
- yes (5)
Is part of the Bibliography
- yes (5)
Institute
- Technik (5)
Publisher
- IEEE (3)
- Springer (1)
- VDE Verlag GmbH (1)
Reduction of power consumption of digital systems is a major concern especially in modern smart sensor systems. These systems are often only activated on request and their power consumption is therefore dominated by the idle-mode. Power reduction mechanisms such as clock or power gating reduce the activity or leakage in the purely digital circuits. We propose a novel adaptive clocking scheme that optimizes the energy demand using a fine-grained oscillator control on cycle-level. To evaluate our new approach, we analytically analyze the power consumption of the regarded system in comparison with available methods. The power of our new adaptive clocking is shown in an integrated smart sensor for capacitive measurements working in a passive wireless sensor node. Using our methods, we show that the energy demand of the example system is reduced even in the case of continuous measurements that demand for a high activity in the digital circuitry.
Nowadays robust, energy-efficient multisensor microsystems often come with heavily restricted power budgets and the characteristic of remaining in certain states for a longer period of time. During this time frame there is no continuous clock signal required which gives the opportunity to suspend the clock until a new transition is requested. In this paper, we present a new topology for on-demand locally clocked finite state machines. The architecture combines a local adaptive clocking approach with synchronous and asynchronous components forming a quasi synchronous system. Using adaptive and local clocking comes with the advantages of reducing the power consumption while saving design effort when no global clock tree is needed. Combining synchronous and asynchronous components is beneficial compared to previous fully asynchronous approaches concerning the design restrictions. The developed topology is verified by the implementation and simulation of a temperature-ADC sensor system realized in a 180 nm process.
Virtual prototyping of integrated mixed-signal smart-sensor systems requires high-performance co-simulation of analog frontend circuitry with complex digital controller hardware and embedded real-time software. We use SystemC/TLM 2.0 in combination with a cycle-count accurate temporal decoupling approach to simulate digital components and firmware code execution at high speed while preserving clock cycle accuracy and, thus, real-time behavior at time quantum boundaries. Optimal time quanta ensuring real-time capability can be calculated and set automatically during simulation if the simulation engine has access to exact timing information about upcoming communication events. These methods fail in case of non-deterministic, asynchronous events resulting in a possibly invalid simulation result. In this paper, we propose an extension of this method to the case of asynchronous events generated by blackbox sources from which a-priori event timing information is not available, such as coupled analog simulators or hardware in the loop. Additional event processing latency and/or rollback effort caused by temporal decoupling is minimized by calculating optimal time quanta dynamically in a SystemC model using a linear prediction scheme. For an example smart-sensor system model, we show that quasi- periodic events that trigger activities in temporally decoupled processes are handled accurately after the predictor has settled.
Analog-/Mixed-Signal (AMS) design verification is one of the most challenging and time consuming tasks of todays complex system on chip (SoC) designs. In contrast to digital system design, AMS designers have to deal with a continuous state space of conservative quantities, highly nonlinear relationships, non-functional influences, etc. enlarging the number of possibly critical scenarios to infinity. In this special session we demonstrate the verification of functional properties using simulative and formal methods. We combine different approaches including automated abstraction and refinement of mixed-level models, state-space discretization as well as affine arithmetic. To reach sufficient verification coverage with reasonable time and effort, we use enhanced simulation schemes to avoid conventional simulation drawbacks.
Virtual prototyping of integrated mixed-signal smart sensor systems requires high-performance co-simulation of analog frontend circuitry with complex digital controller hardware and embedded real-time software. We use SystemC/TLM 2.0 in conjunction with a cycle-count accurate temporal decoupling approach (TD) to simulate digital components and firmware code execution at high speed while preserving clock-cycle accuracy and, thus, real-time behavior at time quantum boundaries. Optimal time quanta ensuring real-time capability can be calculated and set automatically during simulation if the simulation engine has access to exact timing information about upcoming inter-process communication events. These methods fail in the case of non-deterministic, asynchronous events, resulting in potentially invalid simulation results. In this paper, we propose an extension to the case of asynchronous events generated by blackbox sources from which a priori event timing information is not available, such as coupled analog simulators or hardware in the loop. Additional event processing latency or rollback effort caused by temporal decoupling is minimized by calculating optimal time quanta dynamically in a SystemC model using a linear prediction scheme. We analyze the theoretical performance of the presented predictive temporal decoupling approach (PTD) by deriving a cost model that expresses the expected simulation effort in terms of key parameters such as time quantum size and CPU time per simulation cycle. For an exemplary smart-sensor system model, we show that quasi-periodic events that trigger activities in TD processes are handled accurately after the predictor has settled.
