The importance of accurate battery models for power assessment in smart energy systems

The smart energy system is characterized by a broader combination of various energy sources and energy storage devices with smart control management and increased attention to optimization for increasing energy efficiency. The fundamental dimension in the smart energy system design is the power assessment of the possible design architecture. This demand imposes a need for accurately tracking the system’s power flow, simulating and validating the system’s behavior, and applying additional optimization and exploration during the design time. Thus, it is evident that simulation is a critical step in the design flow of a smart energy system. One essential element to enable such accurate simulation is the precise model of the power generation and consumption. While sophisticated models for energy sources exist, the power flow in the system does not perfectly match the power drawn from the energy storage devices because the battery, as the primary energy storage device in the smart energy system, has non-ideal discharge characteristics. We propose adopting an elaborate battery model for the smart energy system’s accurate power assessment in this work. We show the importance of battery model accuracy when conducting a power assessment using two different case studies

Empirical derivation of upper and lower bounds of NBTI aging for embedded cores

In deeply scaled CMOS technologies, device aging causes transistor performance parameters to degrade over time. While reliable models to accurately assess these degradations are available for devices and circuits, the extension to these models for estimating the aging of microprocessor cores is not trivial and there is no well accepted model in the literature. This work proposes a methodology for deriving an NBTI-induced aging model for embedded cores. Since aging can only be determined on a netlist, we use an empirical approach based on characterizing the model using a set of open synthesizable embedded cores, which allows us to establish a link between the aging at the transistor level and the aging from the core perspective in terms of maximum frequency degradation. Using this approach, we were able to (1) prove the independence of the aging on the workloads which run by the cores, and (2) calculate upper and lower bounds for the “aging factor” that can be used for a generic embedded processor. Results show that our method yields very good accuracy in predicting the frequency degradation of cores due to NBTI aging effect, and can be used with confidence when the netlist of the cores is not available

Resource Allocation in the RIS Assisted SCMA Cellular Network Coexisting with D2D Communications

The cellular network coexisting with device-to-device (D2D) communications has been studied extensively. Reconfigurable intelligent surface (RIS) and non-orthogonal multiple access (NOMA) are promising technologies for the evolution of 5G, 6G and beyond. Besides, sparse code multiple access (SCMA) is considered suitable for next-generation wireless network in code-domain NOMA. In this paper, we consider the RIS-aided uplink SCMA cellular network simultaneously with D2D users. We formulate the optimization problem which aims to maximize the cellular sum-rate by jointly designing D2D users resource block (RB) association, the transmitted power for both cellular users and D2D users, and the phase shifts at the RIS. The power limitation and users communication requirements are considered. The problem is non-convex, and it is challenging to solve it directly. To handle this optimization problem, we propose an efficient iterative algorithm based on block coordinate descent (BCD) method. The original problem is decoupled into three subproblems to solve separately. Simulation results demonstrate that the proposed scheme can significantly improve the sum-rate performance over various schemes.Comment: IEEE Acces

SystemC-AMS thermal modeling for the co-simulation of functional and extra-functional properties

Temperature is a critical property of smart systems, due to its impact on reliability and to its inter-dependence with power consumption. Unfortunately, the current design flows evaluate thermal evolution ex-post, on offline power traces. This does not allow to consider temperature as a dimension in the design loop, and it misses all the complex inter-dependencies with design choices and power evolution. In this paper, by adopting the functional language SystemC-AMS, we propose a method to enable thermal/power/functional co-simulation. The system thermal model is built by using state-of-the-art circuit equivalent models, by exploiting the support for electrical linear networks intrinsic of SystemC-AMS. The experimental results will show that the choice of SystemC-AMS is a winning strategy for building a simultaneous simulation of multiple functional and extra-functional properties of a system. The generated code exposes an accuracy comparable to that of the reference thermal simulator HotSpot. Additionally, the initial overhead due to the general purpose nature of SystemC-AMS is compensated by surprisingly high performance of transient simulation, with speedups as high as two orders of magnitude

A Li-ion battery charge protocol with optimal aging-quality of service trade-off

The reduction of usable capacity of rechargeable batteries can be mitigated during the charge process by acting on some stress factors, namely, the average state-of-charge (SOC) and the charge current. Larger values of these quantities cause an increased degradation of battery capacity, so it would be desirable to keep both as low as possible, which is obviously in contrast with the objective of a fast charge. However, by exploiting the fact that in most battery-powered systems the time during which it is plugged for charging largely exceeds the time required to charge, it is possible to devise appropriate charge protocols that achieve a good balance between fast charge and aging. In this paper we propose a charge protocol that, using an accurate estimate of the charging time of a battery and the statistical properties of the charge/discharge patterns, yields an optimal trade-off between aging and quality of service. The latter is measured in terms of the distance of the actual SOC from 100% at the end of the charge phase. Results show that the present method improves significantly over other similar protocols proposed in the literature

LipsFormer: Introducing Lipschitz Continuity to Vision Transformers

We present a Lipschitz continuous Transformer, called LipsFormer, to pursue training stability both theoretically and empirically for Transformer-based models. In contrast to previous practical tricks that address training instability by learning rate warmup, layer normalization, attention formulation, and weight initialization, we show that Lipschitz continuity is a more essential property to ensure training stability. In LipsFormer, we replace unstable Transformer component modules with Lipschitz continuous counterparts: CenterNorm instead of LayerNorm, spectral initialization instead of Xavier initialization, scaled cosine similarity attention instead of dot-product attention, and weighted residual shortcut. We prove that these introduced modules are Lipschitz continuous and derive an upper bound on the Lipschitz constant of LipsFormer. Our experiments show that LipsFormer allows stable training of deep Transformer architectures without the need of careful learning rate tuning such as warmup, yielding a faster convergence and better generalization. As a result, on the ImageNet 1K dataset, LipsFormer-Swin-Tiny based on Swin Transformer training for 300 epochs can obtain 82.7\% without any learning rate warmup. Moreover, LipsFormer-CSwin-Tiny, based on CSwin, training for 300 epochs achieves a top-1 accuracy of 83.5\% with 4.7G FLOPs and 24M parameters. The code will be released at \url{https://github.com/IDEA-Research/LipsFormer}.Comment: To appear in ICLR 2023, our code will be public at https://github.com/IDEA-Research/LipsForme