HALLS: An Energy-Efficient Highly Adaptable Last Level STT-RAM Cache for Multicore Systems

Spin-Transfer Torque RAM (STT-RAM) is widely considered a promising alternative to SRAM in the memory hierarchy due to STT-RAM's non-volatility, low leakage power, high density, and fast read speed. The STT-RAM's small feature size is particularly desirable for the last-level cache (LLC), which typically consumes a large area of silicon die. However, long write latency and high write energy still remain challenges of implementing STT-RAMs in the CPU cache. An increasingly popular method for addressing this challenge involves trading off the non-volatility for reduced write speed and write energy by relaxing the STT-RAM's data retention time. However, in order to maximize energy saving potential, the cache configurations, including STT-RAM's retention time, must be dynamically adapted to executing applications' variable memory needs. In this paper, we propose a highly adaptable last level STT-RAM cache (HALLS) that allows the LLC configurations and retention time to be adapted to applications' runtime execution requirements. We also propose low-overhead runtime tuning algorithms to dynamically determine the best (lowest energy) cache configurations and retention times for executing applications. Compared to prior work, HALLS reduced the average energy consumption by 60.57% in a quad-core system, while introducing marginal latency overhead.Comment: To Appear on IEEE Transactions on Computers (TC

A survey of emerging architectural techniques for improving cache energy consumption

The search goes on for another ground breaking phenomenon to reduce the ever-increasing disparity between the CPU performance and storage. There are encouraging breakthroughs in enhancing CPU performance through fabrication technologies and changes in chip designs but not as much luck has been struck with regards to the computer storage resulting in material negative system performance. A lot of research effort has been put on finding techniques that can improve the energy efficiency of cache architectures. This work is a survey of energy saving techniques which are grouped on whether they save the dynamic energy, leakage energy or both. Needless to mention, the aim of this work is to compile a quick reference guide of energy saving techniques from 2013 to 2016 for engineers, researchers and students

A Survey of Techniques For Improving Energy Efficiency in Embedded Computing Systems

Recent technological advances have greatly improved the performance and features of embedded systems. With the number of just mobile devices now reaching nearly equal to the population of earth, embedded systems have truly become ubiquitous. These trends, however, have also made the task of managing their power consumption extremely challenging. In recent years, several techniques have been proposed to address this issue. In this paper, we survey the techniques for managing power consumption of embedded systems. We discuss the need of power management and provide a classification of the techniques on several important parameters to highlight their similarities and differences. This paper is intended to help the researchers and application-developers in gaining insights into the working of power management techniques and designing even more efficient high-performance embedded systems of tomorrow

A Study on Performance and Power Efficiency of Dense Non-Volatile Caches in Multi-Core Systems

In this paper, we present a novel cache design based on Multi-Level Cell Spin-Transfer Torque RAM (MLC STTRAM) that can dynamically adapt the set capacity and associativity to use efficiently the full potential of MLC STTRAM. We exploit the asymmetric nature of the MLC storage scheme to build cache lines featuring heterogeneous performances, that is, half of the cache lines are read-friendly, while the other is write-friendly. Furthermore, we propose to opportunistically deactivate ways in underutilized sets to convert MLC to Single-Level Cell (SLC) mode, which features overall better performance and lifetime. Our ultimate goal is to build a cache architecture that combines the capacity advantages of MLC and performance/energy advantages of SLC. Our experiments show an improvement of 43% in total numbers of conflict misses, 27% in memory access latency, 12% in system performance, and 26% in LLC access energy, with a slight degradation in cache lifetime (about 7%) compared to an SLC cache

DeepNVM++: Cross-Layer Modeling and Optimization Framework of Non-Volatile Memories for Deep Learning

Non-volatile memory (NVM) technologies such as spin-transfer torque magnetic random access memory (STT-MRAM) and spin-orbit torque magnetic random access memory (SOT-MRAM) have significant advantages compared to conventional SRAM due to their non-volatility, higher cell density, and scalability features. While previous work has investigated several architectural implications of NVM for generic applications, in this work we present DeepNVM++, a framework to characterize, model, and analyze NVM-based caches in GPU architectures for deep learning (DL) applications by combining technology-specific circuit-level models and the actual memory behavior of various DL workloads. We present both iso-capacity and iso-area performance and energy analysis for systems whose last-level caches rely on conventional SRAM and emerging STT-MRAM and SOT-MRAM technologies. In the iso-capacity case, STT-MRAM and SOT-MRAM provide up to 3.8x and 4.7x energy-delay product (EDP) reduction and 2.4x and 2.8x area reduction compared to conventional SRAM, respectively. Under iso-area assumptions, STT-MRAM and SOT-MRAM provide up to 2x and 2.3x EDP reduction and accommodate 2.3x and 3.3x cache capacity when compared to SRAM, respectively. We also perform a scalability analysis and show that STT-MRAM and SOT-MRAM achieve orders of magnitude EDP reduction when compared to SRAM for large cache capacities. Our comprehensive cross-layer framework is demonstrated on STT-/SOT-MRAM technologies and can be used for the characterization, modeling, and analysis of any NVM technology for last-level caches in GPUs for DL applications.Comment: 12 pages, 10 figure

Computing with Spintronics: Circuits and architectures

This thesis makes the following contributions towards the design of computing platforms with spintronic devices. 1) It explores the use of spintronic memories in the design of a domain-specific processor for an emerging class of data-intensive applications, namely recognition, mining and synthesis (RMS). Two different spintronic memory technologies — Domain Wall Memory (DWM) and STT-MRAM — are utilized to realize the different levels in the memory hierarchy of the domain-specific processor, based on their respective access characteristics. Architectural tradeoffs created by the use of spintronic memories are analyzed. The proposed design achieves 1.5X-4X improvements in energy-delay product compared to a CMOS baseline. 2) It describes the first attempt to use DWM in the cache hierarchy of general-purpose processors. DWM promises unparalleled density by packing several bits of data into each bit-cell. TapeCache, the proposed DWM-based cache architecture, utilizes suitable circuit and architectural optimizations to address two key challenges (i) the high energy and latency requirement of write operations and (ii) the need for shift operations to access the data stored in each DWM bit-cell. At the circuit level, DWM bit-cells that are tailored to the distinct design requirements of different levels in the cache hierarchy are proposed. At the architecture level, TapeCache proposes suitable cache organization and management policies to alleviate the performance impact of shift operations required to access data stored in DWM bit-cells. TapeCache achieves more than 7X improvements in both cache area and energy with virtually identical performance compared to an SRAM-based cache hierarchy. 3) It investigates the design of the on-chip memory hierarchy of general-purpose graphics processing units (GPGPUs)—massively parallel processors that are optimized for data-intensive high-throughput workloads—using DWM. STAG, a high density, energy-efficient Spintronic- Tape Architecture for GPGPU cache hierarchies is described. STAG utilizes different DWM bit-cells to realize different memory arrays in the GPGPU cache hierarchy. To address the challenge of high access latencies due to shifts, STAG predicts upcoming cache accesses by leveraging unique characteristics of GPGPU architectures and workloads, and prefetches data that are both likely to be accessed and require large numbers of shift operations. STAG achieves 3.3X energy reduction and 12.1% performance improvement over CMOS SRAM under iso-area conditions. 4) While the potential of spintronic devices for memories is widely recognized, their utility in realizing logic is much less clear. The thesis presents Spintastic, a new paradigm that utilizes Stochastic Computing (SC) to realize spintronic logic. In SC, data is encoded in the form of pseudo-random bitstreams, such that the probability of a \u271\u27 in a bitstream corresponds to the numerical value that it represents. SC can enable compact, low-complexity logic implementations of various arithmetic functions. Spintastic establishes the synergy between stochastic computing and spin-based logic by demonstrating that they mutually alleviate each other\u27s limitations. On the one hand, various building blocks of SC, which incur significant overheads in CMOS implementations, can be efficiently realized by exploiting the physical characteristics of spin devices. On the other hand, the reduced logic complexity and low logic depth of SC circuits alleviates the shortcomings of spintronic logic. Based on this insight, the design of spin-based stochastic arithmetic circuits, bitstream generators, bitstream permuters and stochastic-to-binary converter circuits are presented. Spintastic achieves 7.1X energy reduction over CMOS implementations for a wide range of benchmarks from the image processing, signal processing, and RMS application domains. 5) In order to evaluate the proposed spintronic designs, the thesis describes various device-to-architecture modeling frameworks. Starting with devices models that are calibrated to measurements, the characteristics of spintronic devices are successively abstracted into circuit-level and architectural models, which are incorporated into suitable simulation frameworks. (Abstract shortened by UMI.