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Static random-access memory

About: Static random-access memory is a research topic. Over the lifetime, 10928 publications have been published within this topic receiving 159033 citations. The topic is also known as: static RAM & SRAM.


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Journal ArticleDOI
18 Jun 2016
TL;DR: In this paper, the authors proposed an energy efficient inference engine (EIE) that performs inference on a compressed network model and accelerates the resulting sparse matrix-vector multiplication with weight sharing.
Abstract: State-of-the-art deep neural networks (DNNs) have hundreds of millions of connections and are both computationally and memory intensive, making them difficult to deploy on embedded systems with limited hardware resources and power budgets. While custom hardware helps the computation, fetching weights from DRAM is two orders of magnitude more expensive than ALU operations, and dominates the required power.Previously proposed 'Deep Compression' makes it possible to fit large DNNs (AlexNet and VGGNet) fully in on-chip SRAM. This compression is achieved by pruning the redundant connections and having multiple connections share the same weight. We propose an energy efficient inference engine (EIE) that performs inference on this compressed network model and accelerates the resulting sparse matrix-vector multiplication with weight sharing. Going from DRAM to SRAM gives EIE 120× energy saving; Exploiting sparsity saves 10×; Weight sharing gives 8×; Skipping zero activations from ReLU saves another 3×. Evaluated on nine DNN benchmarks, EIE is 189× and 13× faster when compared to CPU and GPU implementations of the same DNN without compression. EIE has a processing power of 102 GOPS working directly on a compressed network, corresponding to 3 TOPS on an uncompressed network, and processes FC layers of AlexNet at 1.88×104 frames/sec with a power dissipation of only 600mW. It is 24,000× and 3,400× more energy efficient than a CPU and GPU respectively. Compared with DaDianNao, EIE has 2.9×, 19× and 3× better throughput, energy efficiency and area efficiency.

2,445 citations

Proceedings ArticleDOI
20 Jun 2009
TL;DR: This paper analyzes a PCM-based hybrid main memory system using an architecture level model of PCM and proposes simple organizational and management solutions of the hybrid memory that reduces the write traffic to PCM, boosting its lifetime from 3 years to 9.7 years.
Abstract: The memory subsystem accounts for a significant cost and power budget of a computer system. Current DRAM-based main memory systems are starting to hit the power and cost limit. An alternative memory technology that uses resistance contrast in phase-change materials is being actively investigated in the circuits community. Phase Change Memory (PCM) devices offer more density relative to DRAM, and can help increase main memory capacity of future systems while remaining within the cost and power constraints.In this paper, we analyze a PCM-based hybrid main memory system using an architecture level model of PCM.We explore the trade-offs for a main memory system consisting of PCMstorage coupled with a small DRAM buffer. Such an architecture has the latency benefits of DRAM and the capacity benefits of PCM. Our evaluations for a baseline system of 16-cores with 8GB DRAM show that, on average, PCM can reduce page faults by 5X and provide a speedup of 3X. As PCM is projected to have limited write endurance, we also propose simple organizational and management solutions of the hybrid memory that reduces the write traffic to PCM, boosting its lifetime from 3 years to 9.7 years.

1,451 citations

Journal ArticleDOI
David J. Frank1, R.H. Dennard1, E. J. Nowak1, Paul M. Solomon1, Yuan Taur1, Hon-Sum Philip Wong1 
01 Mar 2001
TL;DR: The end result is that there is no single end point for scaling, but that instead there are many end points, each optimally adapted to its particular applications.
Abstract: This paper presents the current state of understanding of the factors that limit the continued scaling of Si complementary metal-oxide-semiconductor (CMOS) technology and provides an analysis of the ways in which application-related considerations enter into the determination of these limits. The physical origins of these limits are primarily in the tunneling currents, which leak through the various barriers in a MOS field-effect transistor (MOSFET) when it becomes very small, and in the thermally generated subthreshold currents. The dependence of these leakages on MOSFET geometry and structure is discussed along with design criteria for minimizing short-channel effects and other issues related to scaling. Scaling limits due to these leakage currents arise from application constraints related to power consumption and circuit functionality. We describe how these constraints work out for some of the most important application classes: dynamic random access memory (DRAM), static random access memory (SRAM), low-power portable devices, and moderate and high-performance CMOS logic. As a summary, we provide a table of our estimates of the scaling limits for various applications and device types. The end result is that there is no single end point for scaling, but that instead there are many end points, each optimally adapted to its particular applications.

1,417 citations

Journal ArticleDOI
TL;DR: NVSim is developed, a circuit-level model for NVM performance, energy, and area estimation, which supports various NVM technologies, including STT-RAM, PCRAM, ReRAM, and legacy NAND Flash and is expected to help boost architecture-level NVM-related studies.
Abstract: Various new nonvolatile memory (NVM) technologies have emerged recently. Among all the investigated new NVM candidate technologies, spin-torque-transfer memory (STT-RAM, or MRAM), phase-change random-access memory (PCRAM), and resistive random-access memory (ReRAM) are regarded as the most promising candidates. As the ultimate goal of this NVM research is to deploy them into multiple levels in the memory hierarchy, it is necessary to explore the wide NVM design space and find the proper implementation at different memory hierarchy levels from highly latency-optimized caches to highly density- optimized secondary storage. While abundant tools are available as SRAM/DRAM design assistants, similar tools for NVM designs are currently missing. Thus, in this paper, we develop NVSim, a circuit-level model for NVM performance, energy, and area estimation, which supports various NVM technologies, including STT-RAM, PCRAM, ReRAM, and legacy NAND Flash. NVSim is successfully validated against industrial NVM prototypes, and it is expected to help boost architecture-level NVM-related studies.

1,100 citations

Journal ArticleDOI
TL;DR: In this article, a design technique for storage elements which are insensitive to radiation-induced single-event upsets is proposed for implementation in high density ASICs and static RAMs using submicron CMOS technology.
Abstract: A novel design technique is proposed for storage elements which are insensitive to radiation-induced single-event upsets. This technique is suitable for implementation in high density ASICs and static RAMs using submicron CMOS technology.

1,096 citations


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Performance
Metrics
No. of papers in the topic in previous years
YearPapers
2023342
2022681
2021354
2020450
2019516
2018503