Reduce Memory Power Consumption Research Articles

Supporting Swap in Real-Time Task Scheduling for Unified Power-Saving in CPU and Memory

As the size of data grows rapidly in modern IoT (Internet-of-Things) and CPS (Cyber-Physical System) applications, the memory power consumption of real-time embedded systems increases dramatically. Unlike general-purpose systems where memory consumes about 10% of the CPU power consumption, modern real-time systems have the memory power of 20–50% of CPU power. This is because the memory size of a real-time system should be large enough to accommodate the entire task set, and thus DRAM refresh operations become a major source of power consumption. In this article, we present a new swap scheme for real-time systems, which aims at reducing memory power consumption. To support swap with real-time constraints, we adopt high-speed NVM storage and co-optimize power-savings in CPU and memory. Unlike traditional real-time task models that only consider the executions in CPU, we define an extended task model that characterizes memory and storage paths of tasks as well, and tightly evaluate the worst-case execution time by formulating the overlapped latency between CPU and memory. By optimizing the CPU supply voltage and the memory swap ratio of given task set, our scheme reduces the energy consumption of real-time systems by 31.1% on average under various workload conditions.

PredictNcool

Recent research on mitigating thermal problems in 3D memories has covered reactive strategies that reduce memory power consumption, and thereby, performance, when the memory temperature reaches the maximum operating limit. Such techniques could benefit from temperature prediction and avoid unnecessary invocations and state transitions of the thermal management strategy. We develop an accurate steady state temperature predictor for thermal management of 3D memories. We utilize the symmetries in the floorplan, along with other design insights, to reduce the predictor’s model parameters, making it lightweight and suitable for runtime thermal management. Using the temperature prediction, we introduce PredictNcool , a proactive thermal management strategy to reduce application runtime and memory energy. We compare PredictNcool with two recent thermal management strategies and our experiments show that the proposed optimization results in performance improvements of 28% and 5%, and memory subsystem energy reductions of 38% and 12% (on average).

A Performance Conserving Approach for Reducing Memory Power Consumption in Multi-Core Systems

With more cores integrated into a single chip and the fast growth of main memory capacity, the DRAM memory design faces ever increasing challenges. Previous studies have shown that DRAM can consume up to 40% of the system power, which makes DRAM a major factor constraining the whole system’s growth in performance. Moreover, memory accesses from different applications are usually interleaved and interfere with each other, which further exacerbates the situation in memory system management. Therefore, reducing memory power consumption has become an urgent problem to be solved in both academia and industry. In this paper, we first proposed a novel strategy called Dynamic Bank Partitioning (DBP), which allocates banks to different applications based on their memory access characteristics. DBP not only effectively eliminates the interference among applications, but also fully takes advantage of bank level parallelism. Secondly, to further reduce power consumption, we propose an adaptive method to dynamically select an optimal page policy for each bank according to the characteristics of memory accesses that each bank receives. Our experimental results show that our strategy not only improves the system performance but also reduces the memory power consumption at the same time. Our proposed scheme can reduce memory power consumption up to 21.2% (10% on average across all workloads) and improve the performance to some extent. In the case that workloads are built with mixed applications, our scheme reduces the power consumption by 14% on average and improves the performance up to 12.5% (3% on average).

Design and Implement of Low Power Consumption SRAM Based on Single Port Sense Amplifier in 65 nm

With the rapid development of integrated circuits [1], low power consumption has become a constant pursuiting goal of the designer in chip design. As the memory almost takes up the area of the chip, reducing memory power consumption will significantly reduce the overall power consumption of the chip; according to ISSCC’s 2014 report about technology trends discussions, there two points of the super-low power SRAM design: 1) design a more effective static and dynamic power control circuit for each key module of SRAM; 2) ensure that in the case of the very low VDD min, SRAM can operating reliably and stably. This paper makes full use reliable of 8T cell, and the single-port sense amplifier has solved problems in the traditional 8T cell structure, making the new structure of the memory at a greater depth still maintain good performance and lower power consumption. Compared with the designed SRAM the SRAM generated by commercial compiler, as the performance loss at SS corner does not exceed 10%, the whole power consumption could be reduced by 54.2%, which can achieve a very good effect of low-power design.

Mini-Rank: A Power-EfficientDDRx DRAM Memory Architecture

Memory power consumption has become a severe concern in multi-core computer platforms. As memory data rate, capacity and bandwidth are being pushed higher and higher, the power consumption of memory systems becomes a significant part in the overall system power profile. Conventional memory systems do not provide an efficient mechanism for managing its power and performance tradeoff. We propose a novel mini-rank architecture for DDRx memories to reduce memory power consumption by breaking each DRAM rank into multiple narrow mini-ranks and activating fewer devices for each request. We also propose a heterogeneous mini-rank design to further improve the performance-power tradeoff for each workload based on its memory access behavior and bandwidth requirement. The evaluation results show that homogeneous mini-rank significantly reduces memory power with small performance loss. For instance, using four-core multiprogramming workloads, a x32 mini-rank configuration reduces memory power by 19.5 percent with 1.3 percent performance loss on average for memory-intensive workloads. Heterogeneous mini-rank further improves the balance between the performance and power saving. For instance, it reduces the memory power by up to 38.0 percent with an average performance loss of 2.4 percent, compared with a conventional memory system. In comparison, the x32 homogeneous mini-rank reduces memory power by up to 25.4 percent; while the x8 homogeneous mini-rank incurs performance loss by up to 19.3 percent. Furthermore, heterogeneous mini-rank achieves consistently good performance-power tradeoff for workloads made by programs of diverse memory access behavior and bandwidth requirement.

Coordinating DRAM and Last-Level-Cache Policies with the Virtual Write Queue

To alleviate bottlenecks in this era of many-core architectures, the authors propose a virtual write queue to expand the memory controller's scheduling window through visibility of cache behavior. Awareness of the physical main memory layout and a focus on writes can shorten both read and write average latency, reduce memory power consumption, and improve overall system performance.