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

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

An Adaptive ECC Scheme for Runtime Write Failure Suppression of STT-RAM Cache

Spin-transfer torque random access memory (STT-RAM) features many attractive charac- teristics, including near-zero standby power, nanosecond access time, small footprint, etc. These properties make STT-RAM perfectly suitable for the applications that are subject to limited power and area budgets, i.e., on-chip cache. Write reliability is one of the major challenges in design of STT-RAM caches. To ensure design quality, error correction code (ECC) scheme is usually adopted in STT-RAM caches. However, it incurs significant hard- ware overhead. In observance of the dynamic error correcting requirements, in this work, we propose an adaptive ECC scheme to suppress the runtime write failures of STT-RAM cache with minimized hardware cost, in which the cache is partitioned into regions protected by different ECCs. The error rate of a data is speculated on-the-fly and the data is allocated to a partition that provides the needed error correcting capability. Moreover, to accom- modate the time-varying error correcting requirements of runtime data, the thresholds that determine data’s destination cache partition will be adaptively adjusted. Our experimental results show that compared to conventional ECC schemes, our scheme can save up to 80.2% ECC bit overhead with slightly degraded write reliability of the STT-RAM cache. Moreover, the detailed analysis shows that through simultaneous optimization in cache access patterns and reducing STT cell programming workload, our method outperforms conventional ECC design in power and energy consumptions

TSV placement optimization for liquid cooled 3D-ICs with emerging NVMs

Three dimensional integrated circuits (3D-ICs) are a promising solution to the performance bottleneck in planar integrated circuits. One of the salient features of 3D-ICs is their ability to integrate heterogeneous technologies such as emerging non-volatile memories (NVMs) in a single chip. However, thermal management in 3D-ICs is a significant challenge, owing to the high heat flux (~ 250 W/cm2). Several research groups have focused either on run-time or design-time mechanisms to reduce the heat flux and did not consider 3D-ICs with heterogeneous stacks. The goal of this work is to achieve a balanced thermal gradient in 3D-ICs, while reducing the peak temperatures. In this research, placement algorithms for design-time optimization and choice of appropriate cooling mechanisms for run-time modulation of temperature are proposed. Specifically, an architectural framework which introduce weight-based simulated annealing (WSA) algorithm for thermal-aware placement of through silicon vias (TSVs) with inter-tier liquid cooling is proposed for design-time. In addition, integrating a dedicated stack of emerging NVMs such as RRAM, PCRAM and STTRAM, a run-time simulation framework is developed to analyze the thermal and performance impact of these NVMs in 3D-MPSoCs with inter-tier liquid cooling. Experimental results of WSA algorithm implemented on MCNC91 and GSRC benchmarks demonstrate up to 11 K reduction in the average temperature across the 3D-IC chip. In addition, power density arrangement in WSA improved the uniformity by 5%. Furthermore, simulation results of PARSEC benchmarks with NVM L2 cache demonstrates a temperature reduction of 12.5 K (RRAM) compared to SRAM in 3D-ICs. Especially, RRAM has proved to be thermally efficient replacement for SRAM with 34% lower energy delay product (EDP) and 9.7 K average temperature reduction

Energy-Aware Data Movement In Non-Volatile Memory Hierarchies

While technology scaling enables increased density for memory cells, the intrinsic high leakage power of conventional CMOS technology and the demand for reduced energy consumption inspires the use of emerging technology alternatives such as eDRAM and Non-Volatile Memory (NVM) including STT-MRAM, PCM, and RRAM. The utilization of emerging technology in Last Level Cache (LLC) designs which occupies a signifcant fraction of total die area in Chip Multi Processors (CMPs) introduces new dimensions of vulnerability, energy consumption, and performance delivery. To be specific, a part of this research focuses on eDRAM Bit Upset Vulnerability Factor (BUVF) to assess vulnerable portion of the eDRAM refresh cycle where the critical charge varies depending on the write voltage, storage and bit-line capacitance. This dissertation broaden the study on vulnerability assessment of LLC through investigating the impact of Process Variations (PV) on narrow resistive sensing margins in high-density NVM arrays, including on-chip cache and primary memory. Large-latency and power-hungry Sense Amplifers (SAs) have been adapted to combat PV in the past. Herein, a novel approach is proposed to leverage the PV in NVM arrays using Self-Organized Sub-bank (SOS) design. SOS engages the preferred SA alternative based on the intrinsic as-built behavior of the resistive sensing timing margin to reduce the latency and power consumption while maintaining acceptable access time. On the other hand, this dissertation investigates a novel technique to prioritize the service to 1) Extensive Read Reused Accessed blocks of the LLC that are silently dropped from higher levels of cache, and 2) the portion of the working set that may exhibit distant re-reference interval in L2. In particular, we develop a lightweight Multi-level Access History Profiler to effciently identify ERRA blocks through aggregating the LLC block addresses tagged with identical Most Signifcant Bits into a single entry. Experimental results indicate that the proposed technique can reduce the L2 read miss ratio by 51.7% on average across PARSEC and SPEC2006 workloads. In addition, this dissertation will broaden and apply advancements in theories of subspace recovery to pioneer computationally-aware in-situ operand reconstruction via the novel Logic In Interconnect (LI2) scheme. LI2 will be developed, validated, and re?ned both theoretically and experimentally to realize a radically different approach to post-Moore\u27s Law computing by leveraging low-rank matrices features offering data reconstruction instead of fetching data from main memory to reduce energy/latency cost per data movement. We propose LI2 enhancement to attain high performance delivery in the post-Moore\u27s Law era through equipping the contemporary micro-architecture design with a customized memory controller which orchestrates the memory request for fetching low-rank matrices to customized Fine Grain Reconfigurable Accelerator (FGRA) for reconstruction while the other memory requests are serviced as before. The goal of LI2 is to conquer the high latency/energy required to traverse main memory arrays in the case of LLC miss, by using in-situ construction of the requested data dealing with low-rank matrices. Thus, LI2 exchanges a high volume of data transfers with a novel lightweight reconstruction method under specific conditions using a cross-layer hardware/algorithm approach