Fine-grained power estimation in multicore Systems on Chips (SoCs) is crucial for efficient thermal management. BPI (Blind Power Identification) is a recent approach that determines the power consumption of different cores and the thermal model of the chip using only thermal sensor measurements and total power consumption. BPI relies on steady-state thermal data along with a naive initialization in its Non-negative Matrix Factorization (NMF) process, which negatively impacts the power estimation accuracy of BPI. This paper proposes a two-fold approach to reduce these impacts on BPI. First, this paper introduces an innovative approach for NMF initializing, i.e., density-oriented spatial clustering to identify centroid data points of active cores as initial values. This enhances BPI accuracy by focusing on dense regions in the dataset and excluding outlier data points. Second, it proposes the utilization of steady-state temperature data points to enhance the power estimation accuracy by leveraging the physical relationship between temperature and power consumption. Our extensive simulations of real-world cases demonstrate that our approach enhances BPI accuracy in estimating the power per core with no performance cost. For instance, in a four-core processor, the proposed approach reduces the error rate by 76% compared to BPI and by 24% compared to the state of the art in the literature, namely, Blind Power Identification Steady State (BPISS). The results underline the potential of integrating advanced clustering techniques in thermal model identification, paving the way for more accurate and reliable thermal management in multicores and SoCs.
Self-consumption is developing worldwide to increase renewable electricity consumption and reduce electricity bills. It can be carried out individually or collectively (grouping several entities), but is generally restricted geographically. In datacenters, one may use load shifting to benefit from the self-consumption tariffs, at the cost of increasing energy consumption. An alternative would be to extend the self-consumption rules to wider perimeters. This paper proposes a comparative study on several aspects influencing the collective self-consumption (CSC) of an Edge infrastructure, including spatial load shifting, temporal load shifting and extending the current rules to encompass wider geographical boundaries. Spatial shifting under the current CSC scheme is found more cost-effective (3.9% of cost reduction) with a negligible increase in energy consumption (0.19%), compared to the revised definition of collective self-consumption which leads to 3.7% cost reduction and no increase in energy consumption. Moreover, allowing up to 10% of the user tasks to be shifted in time can further increase the self-consumption rate by 1.3%.
Systolic arrays are commonly used for running deep neural networks (DNNs) at the edge, where latency and energy efficiency requirements are stringent. Monolithic 3D (Mono3D) is an emerging 3D integration technology that offers ultra-high vertical interconnect density among processing and memory layers. The bandwidth benefits provided by Mono3D can help meet the growing latency and energy efficiency demands for DNNs. This paper presents a novel implementation for weight stationary (WS) dataflow in Mono3D systolic arrays, called WS-Mono3D. WS-Mono3D utilizes multiple resistive RAM layers and SRAM with high-density vertical interconnects to multicast inputs and performs high-bandwidth weight pre-loading while maintaining the same order of multiply-and-accumulate operations as in native WS dataflow. Consequently, WS-Mono3D eliminates input and weight forwarding cycles, and, thus, provides up to a 40% reduction in energy-delay-product (EDP) over the native WS implementation in 2D with iso-configuration. The paper also demonstrates the impact of temperature on energy efficiency benefits in WS-Mono3D.
Datacenters could greatly improve their sustainability by shifting their power usage across time in response to electricity’s carbon intensity. However, despite decades of research, modulating demand in response to grid signals has not been deployed in wide practice. We review diverse studies and real-world practices to understand the reasons for the gap between concept and reality, identifying several significant challenges. Demand response frameworks (i) are often too complex, (ii) break abstraction layers in datacenter management, (iii) place too much emphasis on batch processing jobs, (iv) lack dynamic strategies, and (v) provide insufficient incentives for datacenter operators and users. Overcoming these challenges is essential to making datacenter demand response a reality, which would lead to sustainable, efficient computing.
Datacenters have been a significant source of global carbon emissions. Researchers have developed tools to quantify the emissions of datacenters, aiming to enable environmentally sustainable datacenter development. However, these tools include only some of the hardware components; focus primarily on the design stage; and do not account for the inherent uncertainties of hardware and software characteristics. To this end, we propose an uncertainty-aware dynamic unified carbon modeling tool, dubbed U-DUCT, which, to the best of our knowledge, is the first attempt to comprehensively understand the datacenter carbon emissions by addressing these limitations. Specifically, U-DUCT considers both well-studied computer servers and the rarely included storage and switches, which have a huge impact on carbon emissions. Moreover, we shift the focus of U-DUCT to the run-time stage and investigate the effect of uncertainties on both embodied and operational carbon estimations. By using U-DUCT to account for uncertainty, experiments on a small-scale cluster running real-world application workloads reveal new potential for reducing carbon emissions from computer servers, storage racks, and switches.
