Area and power constrained edge devices are increasingly utilized to perform compute intensive workloads, necessitating increasingly area and power efficient accelerators. In this context, in-SRAM computing performs hundreds of parallel operations on spatially local data common in many emerging workloads, while reducing power consumption due to data movement. However, in-SRAM computing faces many challenges, including integration into the existing architecture, arithmetic operation support, data corruption at high operating frequencies, inability to run at low voltages, and low area density. To meet these challenges, this work introduces BLADE, a BitLine Accelerator for Devices on the Edge. BLADE is an in-SRAM computing architecture that utilizes local wordline groups to perform computations at a frequency 2.8× higher than state-of-the-art in-SRAM computing architectures. BLADE is integrated into the cache hierarchy of low-voltage edge devices, and simulated and benchmarked at the transistor, architecture, and software abstraction levels. Experimental results demonstrate performance/energy gains over an equivalent NEON accelerated processor for a variety of edge device workloads, namely, cryptography (4× performance gain/6× energy reduction), video encoding (6×/2×), and convolutional neural networks (3×/1.5×), while maintaining the highest frequency/energy ratio (up to 2.2Ghz@1V) of any conventional in-SRAM computing architecture, and a low area overhead of less than 8%.

The increasing power density in modern high-performance multiprocessor System-on-Chip (MPSoC) is fueling a revolution in thermal management. On the one hand, thermal phenomena are becoming a critical concern, making accurate and efficient simulation a necessity. On the other hand, a variety of physically heterogeneous solutions is coming into play: liquid, evaporative, thermoelectric cooling, and more. A new generation of simulators, with unprecedented flexibility, is thus required. In this article, we present 3D-ICE 3.0, the first thermal simulator to allow for accurate nonlinear descriptions of complex and physically heterogeneous heat dissipation systems, while preserving the efficiency of latest compact modeling frameworks at the silicon die level. 3D-ICE 3.0 allows designers to extend the thermal simulator with new heat sink models while simplifying the time-consuming step of model validation. The support for nonlinear dynamic models is included, for instance, to accurately represent variable coolant flows. Our results present validated models of a commercial water heat sink and an air heat sink plus fan that achieve an average error below 1 °C and simulate, respectively, up to $3\times $ and $12\times $ faster than the real physical phenomena.

Improving the energy efficiency of data centers while guaranteeing Quality of Service (QoS), together with detecting performance variability of servers caused by either hardware or software failures, are two of the major challenges for efficient resource management of large-scale cloud infrastructures. Previous works in the area of dynamic Virtual Machine (VM) consolidation are mostly focused on addressing the energy challenge, but fall short in proposing comprehensive, scalable, and low-overhead approaches that jointly tackle energy efficiency and performance variability. Moreover, they usually assume over-simplistic power models, and fail to accurately consider all the delay and power costs associated with VM migration and host power mode transition. These assumptions are no longer valid in modern servers executing heterogeneous workloads and lead to unrealistic or inefficient results. In this paper, we propose a centralized-distributed low-overhead failure-aware dynamic VM consolidation strategy to minimize energy consumption in large-scale data centers. Our approach selects the most adequate power mode and frequency of each host during runtime using a distributed multi-agent Machine Learning (ML) based strategy, and migrates the VMs accordingly using a centralized heuristic. Our Multi-AGent machine learNing-based approach for Energy efficienT dynamIc Consolidation (MAGNETIC) is implemented in a modified version of the CloudSim simulator, and considers the energy and delay overheads associated with host power mode transition and VM migration, and is evaluated using power traces collected from various workloads running in real servers and resource utilization logs from cloud data center infrastructures. Results show how our strategy reduces data center energy consumption by up to 15% compared to other works in the state-of-the-art (SoA), guaranteeing the same QoS and reducing the number of VM migrations and host power mode transitions by up to 86% and 90%, respectively. Moreover, it shows better scalability than all other approaches, taking less than 0.7% time overhead to execute for a data center with 1500 VMs. Finally, our solution is capable of detecting host performance variability due to failures, automatically migrating VMs from failing hosts and draining them from workload.

The computational and cooling power demands of enterprise servers are increasing at an unsustainable rate. Understanding the relationship between computational power, temperature, leakage, and cooling power is crucial to enable energy-efficient operation at the server and data center levels. This paper develops empirical models to estimate the contributions of static and dynamic power consumption in enterprise servers for a wide range of workloads, and analyzes the interactions between temperature, leakage, and cooling power for various workload allocation policies. We propose a cooling management policy that minimizes the server energy consumption by setting the optimum fan speed during runtime. Our experimental results on a presently shipping enterprise server demonstrate that including leakage awareness in workload and cooling management provides additional energy savings without any impact on performance.

Reducing the energy consumption for computation and cooling in servers is a major challenge considering the data center energy costs today. To ensure energy-efficient operation of servers in data centers, the relationship among computational power, t...