Improvement Energy Efficiency for a Hybrid Multibank Memory in Energy Critical Applications

High performance, low power multiprocessor/multibank memory system requires a compiler that provides efficient data partitioning and mapping procedures. This paper introduced two compiler techniques for the data mapping to multibank memory, since data mapping is still an open problem and needs a better solution. The multibank memory can be consisted of volatile and non-volatile memory components to support ultra-low powered wearable devices. This hybrid memory system including volatile and non-volatile memory components yields higher complexity to map data onto it. To efficiently solve this mapping problem, we formulate it to a simple decision problem. Based on the problem definition, we proposed two efficient algorithms to determine the placement of data to the multibank memory. The proposed techniques consider the characteristic of the non-volatile memory that its write operation consumes more energy than the same operation of a volatile memory even though it provides ultra-low operation power and nearly zero leakage current. The proposed technique solves this negative effect of non-volatile memory by using efficient data placement technique and hybrid memory architecture. In experimental section, the result shows that the proposed techniques improve energy saving up to 59.5% for the hybrid multibank memory architecture

Extending Memory Capacity in Consumer Devices with Emerging Non-Volatile Memory: An Experimental Study

The number and diversity of consumer devices are growing rapidly, alongside their target applications' memory consumption. Unfortunately, DRAM scalability is becoming a limiting factor to the available memory capacity in consumer devices. As a potential solution, manufacturers have introduced emerging non-volatile memories (NVMs) into the market, which can be used to increase the memory capacity of consumer devices by augmenting or replacing DRAM. Since entirely replacing DRAM with NVM in consumer devices imposes large system integration and design challenges, recent works propose extending the total main memory space available to applications by using NVM as swap space for DRAM. However, no prior work analyzes the implications of enabling a real NVM-based swap space in real consumer devices. In this work, we provide the first analysis of the impact of extending the main memory space of consumer devices using off-the-shelf NVMs. We extensively examine system performance and energy consumption when the NVM device is used as swap space for DRAM main memory to effectively extend the main memory capacity. For our analyses, we equip real web-based Chromebook computers with the Intel Optane SSD, which is a state-of-the-art low-latency NVM-based SSD device. We compare the performance and energy consumption of interactive workloads running on our Chromebook with NVM-based swap space, where the Intel Optane SSD capacity is used as swap space to extend main memory capacity, against two state-of-the-art systems: (i) a baseline system with double the amount of DRAM than the system with the NVM-based swap space; and (ii) a system where the Intel Optane SSD is naively replaced with a state-of-the-art (yet slower) off-the-shelf NAND-flash-based SSD, which we use as a swap space of equivalent size as the NVM-based swap space

Next-Gen Hybrid Memory and Interconnect System Architectures

This dissertation mainly addresses two problems that emerge along with the 'big data' trend: the increasing demands of memory capacity for mobile computing platform, and the needs for interconnection network with higher bandwidth/energy efficiency in the HPC/Data Center. The current mobile applications have rapidly growing memory footprints, posing a great challenge for memory system design. Insufficient DRAM main memory will incur frequent data swaps between memory and storage, a process that hurts performance, consumes energy and deteriorates the write endurance of typical flash storage devices. Alternately, a larger DRAM has higher leakage power and drains the battery faster. Further, DRAM scaling trends make further growth of DRAM in the mobile space prohibitive due to cost. Emerging non-volatile memory (NVM) has the potential to alleviate these issues due to its higher capacity per cost than DRAM and minimal static power. Recently, a wide spectrum of NVM technologies, including phase-change memories (PCM), memristor, and 3D XPoint have emerged. Despite the mentioned advantages, NVM has longer access latency compared to DRAM and NVM writes can incur higher latencies and wear costs. Therefore integration of these new memory technologies in the memory hierarchy requires a fundamental rearchitecting of traditional system designs. In this work, we propose a hardware-accelerated memory manager (HMMU) that addresses both types of memory in a flat space address space. We design a set of data placement and data migration policies within this memory manager, such that we may exploit the advantages of each memory technology. By augmenting the system with this HMMU, we reduce the overall memory latency while also reducing energy consumption and writes to the NVM. Experimental results show that our design achieves a 39% reduction in energy consumption with only a 12% performance degradation versus an all-DRAM baseline that is likely untenable in the future. After developing the pure hardware memory management for the data migration between DRAM and NVM, we consider to integrate information from the software stack into our system. These software information, such as programmers' hints or application profiling results, reveals the longer-term memory access pattern and data object properties; but they come at the cost of high software latency. Hardware approaches can avoid the latencies of software kernel processes related to page migration, such as page fault handling. However, hardware's vision is limited to a short time window, as it can only monitor and analyze the recently received memory requests. Ideally, the execution time advantages of pure hardware approaches, should be combined with the data object properties in a global scope. Further, application programmer's hints could guide the data placement at the allocation time, thus data objects with similar property could be congregated to reduce unnecessary page migrations. In this work, we propose such a hardware-software cooperative approach. In particular, we built a heap memory manager that allows the programmer to choose the memory type for each data object allocation. Such denotations are relayed to the hardware memory manager as hints for the decisions on data placement and migration. Meanwhile the hardware memory manager is still capable of capturing the per-application phase changes and maintaining flexibility in its data redistribution. The integration of the two mechanisms leads to optimal results from both long-term and short-term aspects. Experiment results show that our design shortens the overall memory latency while also reducing energy consumption and writes to the NVM versus prior approaches. Our design achieves a 40% reduction in energy consumption with only a 16% performance degradation versus the all-DRAM memory system. As for the HPC/Data domain, a primary problem is how to scale up the interconnection network to service the ever-increasing number of nodes. Photonic-links, with its high bandwidth and low signal loss across long distance propagation, is a promising technology to solve this problem. The higher bandwidth allows the router to connect more nodes while the long-distance connection makes it possible to implement more advanced typologies, such as the flattened butterfly. Both factors help to reduce the average number of hops between nodes across the network. Such high-radix and short distance network is essential to provisioning low latency communications in massive scale systems. However, due to the different physical and device properties, interconnection network needs redesign to adopt the photonic links. We first listed the basic formulas and design flow for interconnection network, and introduced a highly efficient event-driven simulator. Then we conducted a series of experiments to explore the design space, and gave a quantitative comparison between interconnection networks made of pure electrical links and those with electronic/photonic hybrid design