International Technology Roadmap for Semiconductors 2003の要求清浄度について － シリコンウエハ表面と雰囲気環境に要求される清浄度, 分析方法の現状について －

2005년 대한 생화학·분자생물학회 하계학술대회를 다녀와서

IEEE transactions on computer-aided design of integrated circuits and systems : a publication of the IEEE Circuits and Systems Society

This paper combines background information about the IBM Journal of Research and Development with guidelines for the preparation of Journal manuscripts. The purpose is to acquaint authors with the Journal as a primary, professional publication and to present suggestions to ease the work of author and editor in preparing clear, concise, and useful manuscripts.

IBM journal of research and development: information for authors

Die-stacked DRAM is a technology that will soon be integrated in high-performance systems. Recent studies have focused on hardware caching techniques to make use of the stacked memory, but these approaches require complex changes to the processor and also cannot leverage the stacked memory to increase the system's overall memory capacity. In this work, we explore the challenges of exposing the stacked DRAM as part of the system's physical address space. This non-uniform access memory (NUMA) styled approach greatly simplifies the hardware and increases the physical memory capacity of the system, but pushes the burden of managing the heterogeneous memory architecture (HMA) to the software layers. We first explore simple (and somewhat impractical) schemes to manage the HMA, and then refine the mechanisms to address a variety of hardware and software implementation challenges. In the end, we present an HMA approach with low hardware and software impact that can dynamically tune itself to different application scenarios, achieving performance even better than the (impractical-to-implement) baseline approaches.

/pdf/heterogeneous-memory-architectures-a-hw-sw-approach-for-3ijqu5z526.pdf

Heterogeneous memory architectures: A HW/SW approach for mixing die-stacked and off-package memories

Racetrack memory (RTM) is a novel spintronic memory-storage technology that has the potential to overcome fundamental constraints of existing memory and storage devices. It is unique in that its core differentiating feature is the movement of data, which is composed of magnetic domain walls (DWs), by short current pulses. This enables more data to be stored per unit area compared to any other current technologies. On the one hand, RTM has the potential for mass data storage with unlimited endurance using considerably less energy than today’s technologies. On the other hand, RTM promises an ultrafast nonvolatile memory competitive with static random access memory (SRAM) but with a much smaller footprint. During the last decade, the discovery of novel physical mechanisms to operate RTM has led to a major enhancement in the efficiency with which nanoscopic, chiral DWs can be manipulated. New materials and artificially atomically engineered thin-film structures have been found to increase the speed and lower the threshold current with which the data bits can be manipulated. With these recent developments, RTM has attracted the attention of the computer architecture community that has evaluated the use of RTM at various levels in the memory stack. Recent studies advocate RTM as a promising compromise between, on the one hand, power-hungry, volatile memories and, on the other hand, slow, nonvolatile storage. By optimizing the memory subsystem, significant performance improvements can be achieved, enabling a new era of cache, graphical processing units, and high capacity memory devices. In this article, we provide an overview of the major developments of RTM technology from both the physics and computer architecture perspectives over the past decade. We identify the remaining challenges and give an outlook on its future.

/pdf/magnetic-racetrack-memory-from-physics-to-the-cusp-of-5brh05qnrb.pdf

Magnetic Racetrack Memory: From Physics to the Cusp of Applications Within a Decade

Racetrack memories (RMs) have significantly evolved since their conception in 2008, making them a serious contender in the field of emerging memory technologies. Despite key technological advancements, the access latency and energy consumption of an RM-based system are still highly influenced by the number of shift operations. These operations are required to move bits to the right positions in the racetracks. This article presents data-placement techniques for RMs that maximize the likelihood that consecutive references access nearby memory locations at runtime, thereby minimizing the number of shifts. We present an integer linear programming (ILP) formulation for optimal data placement in RMs, and we revisit existing offset assignment heuristics, originally proposed for random-access memories. We introduce a novel heuristic tailored to a realistic RM and combine it with a genetic search to further improve the solution. We show a reduction in the number of shifts of up to 52.5%, outperforming the state of the art by up to 16.1%.

/pdf/shiftsreduce-minimizing-shifts-in-racetrack-memory-4-0-5fes9wpkf8.pdf

ShiftsReduce: Minimizing Shifts in Racetrack Memory 4.0

Two key parameters that determine the performance of a DRAM cache based multi-core system are DRAM cache hit latency (HL) and DRAM cache miss rate (MR), as they strongly influence the average DRAM cache access latency. Recently proposed DRAM set mapping policies are either optimized for HL or for MR. None of these policies provides a good HL and MR at the same time. This paper presents a novel DRAM set mapping policy that simultaneously targets both parameters with the goal of achieving the best of both to reduce the overall DRAM cache access latency. For a 16-core system, our proposed set mapping policy reduces the average DRAM cache access latency (depends upon HL and MR) compared to state-of-the-art DRAM set mapping policies that are optimized for either HL or MR by 29.3% and 12.1%, respectively.

Simultaneously optimizing DRAM cache hit latency and miss rate via novel set mapping policies

Recent research has proposed having a die-stacked last-level cache (LLC) to overcome the memory wall. Lately, spin-transfer-torque random access memory (STT-RAM) caches have received attention, since they provide improved energy efficiency compared with DRAM caches. However, recently proposed STT-RAM cache architectures unnecessarily dissipate energy by fetching unneeded cache lines (CLs) into the row buffer (RB). In this paper, we propose a selective read policy for the STT-RAM which fetches those CLs into the RB that are likely to be reused. In addition, we propose a tags-update policy that reduces the number of STT-RAM writebacks. This reduces the number of reads/writes and thereby decreases the energy consumption. To reduce the latency penalty of our selective read policy, we propose the following performance optimizations: 1) an RB tags-bypass policy that reduces STT-RAM access latency; 2) an LLC data cache that stores the CLs that are likely to be used in the near future; 3) an address organization scheme that simultaneously reduces LLC access latency and miss rate; and 4) a tags-to-column mapping policy that improves access parallelism. For evaluation, we implement our proposed architecture in the Zesto simulator and run different combinations of SPEC2006 benchmarks on an eight-core system. We compare our approach with a recently proposed STT-RAM LLC with subarray parallelism support and show that our synergistic policies reduce the average LLC dynamic energy consumption by 75% and improve the system performance by 6.5%. Compared with the state-of-the-art DRAM LLC with subarray parallelism, our architecture reduces the LLC dynamic energy consumption by 82% and improves system performance by 6.8%.

Performance and Energy-Efficient Design of STT-RAM Last-Level Cache

Racetrack memories (RTMs) have drawn considerable attention from computer architects of late. Owing to the ultra-high capacity and comparable access latency to SRAM, RTMs are promising candidates to revolutionize the memory subsystem. In order to evaluate their performance and suitability at various levels in the memory hierarchy, it is crucial to have RTM-specific simulation tools that accurately model their behavior and enable exhaustive design space exploration. To this end, we propose RTSim , an open source cycle-accurate memory simulator that enables performance evaluation of the domain-wall-based racetrack memories. The skyrmions-based RTMs can also be modeled with RTSim because they are architecturally similar to domain-wall-based RTMs. RTSim is developed in collaboration with physicists and computer scientists. It accurately models RTM-specific shift operations , access ports management and the sequence of memory commands beside handling the routine read/write operations. RTSim is built on top of NVMain2.0, offering larger design space for exploration.

/pdf/rtsim-a-cycle-accurate-simulator-for-racetrack-memories-twhe22aax8.pdf

Fazal Hameed

Papers

Magnetic Racetrack Memory: From Physics to the Cusp of Applications Within a Decade

ShiftsReduce: Minimizing Shifts in Racetrack Memory 4.0

Simultaneously optimizing DRAM cache hit latency and miss rate via novel set mapping policies

Performance and Energy-Efficient Design of STT-RAM Last-Level Cache

RTSim: A Cycle-Accurate Simulator for Racetrack Memories