Multi-port Memory Design for Advanced Computer Architectures

In this thesis, we describe and evaluate novel memory designs for multi-port on-chip and off-chip use in advanced computer architectures. We focus on combining multi-porting and evaluating the performance over a range of design parameters. Multi-porting is essential for caches and shared-data systems, especially multi-core System-on-chips (SOC). It can significantly increase the memory access throughput. We evaluate FinFET voltage-mode multi-port SRAM cells using different metrics including leakage current, static noise margin and read/write performance. Simulation results show that single-ended multi-port FinFET SRAMs with isolated read ports offer improved read stability and flexibility over classical double-ended structures at the expense of write performance. By increasing the size of the access transistors, we show that the single-ended multi-port structures can achieve equivalent write performance to the classical double-ended multi-port structure for 9% area overhead. Moreover, compared with CMOS SRAM, FinFET SRAM has better stability and standby power. We also describe new methods for the design of FinFET current-mode multi-port SRAM cells. Current-mode SRAMs avoid the full-swing of the bitline, reducing dynamic power and access time. However, that comes at the cost of voltage drop, which compromises stability. The design proposed in this thesis utilizes the feature of Independent Gate (IG) mode FinFET, which can leverage threshold voltage by controlling the back gate voltage, to merge two transistors into one through high-Vt and low-Vt transistors. This design not only reduces the voltage drop, but it also reduces the area in multi-port current-mode SRAM design. For off-chip memory, we propose a novel two-port 1-read, 1-write (1R1W) phasechange memory (PCM) cell, which significantly reduces the probability of blocking at the bank levels. Different from the traditional PCM cell, the access transistors are at the top and connected to the bitline. We use Verilog-A to model the behavior of Ge2Sb2Te5 (GST: the storage component). We evaluate the performance of the two-port cell by transistor sizing and voltage pumping. Simulation results show that pMOS transistor is more practical than nMOS transistor as the access device when both area and power are considered. The estimated area overhead is 1.7�, compared to single-port PCM cell. In brief, the contribution we make in this thesis is that we propose and evaluate three different kinds of multi-port memories that are favorable for advanced computer architectures

Ultra-low Power FinFET SRAM Cell with improved stability suitable for low power applications

In this paper, a new 11T SRAM cell using FinFET technology has been proposed, the basic component of the cell is the 6T SRAM cell with 4 NMOS access transistors to improve the stability and also makes it a dual port memory cell. The proposed cell uses a header scheme in which one extra PMOS transistor is used which is biased at different voltages to improve the read and write stability thus, helps in reducing the leakage power and active power. The cell shows improvement in RSNM (Read Static Noise Margin) with LP8T by 2.39x at sub-threshold voltage 2.68x with D6T SRAM cell, 5.5x with TG8T. The WSNM (Write Static Noise Margin) and HM (Hold Margin) of the SRAM cell at 0.9V is 306mV and 384mV. At sub-threshold operation also it shows improvement. The Leakage power reduced by 0.125x with LP8T, 0.022x with D6T SRAM cell, TG8T and SE8T. Also, impact of process variation on cell stability is discussed

MorphIC: A 65-nm 738k-Synapse/mm2^2 Quad-Core Binary-Weight Digital Neuromorphic Processor with Stochastic Spike-Driven Online Learning

Recent trends in the field of neural network accelerators investigate weight quantization as a means to increase the resource- and power-efficiency of hardware devices. As full on-chip weight storage is necessary to avoid the high energy cost of off-chip memory accesses, memory reduction requirements for weight storage pushed toward the use of binary weights, which were demonstrated to have a limited accuracy reduction on many applications when quantization-aware training techniques are used. In parallel, spiking neural network (SNN) architectures are explored to further reduce power when processing sparse event-based data streams, while on-chip spike-based online learning appears as a key feature for applications constrained in power and resources during the training phase. However, designing power- and area-efficient spiking neural networks still requires the development of specific techniques in order to leverage on-chip online learning on binary weights without compromising the synapse density. In this work, we demonstrate MorphIC, a quad-core binary-weight digital neuromorphic processor embedding a stochastic version of the spike-driven synaptic plasticity (S-SDSP) learning rule and a hierarchical routing fabric for large-scale chip interconnection. The MorphIC SNN processor embeds a total of 2k leaky integrate-and-fire (LIF) neurons and more than two million plastic synapses for an active silicon area of 2.86mm$^2$ in 65nm CMOS, achieving a high density of 738k synapses/mm$^2$. MorphIC demonstrates an order-of-magnitude improvement in the area-accuracy tradeoff on the MNIST classification task compared to previously-proposed SNNs, while having no penalty in the energy-accuracy tradeoff.Comment: This document is the paper as accepted for publication in the IEEE Transactions on Biomedical Circuits and Systems journal (2019), the fully-edited paper is available at https://ieeexplore.ieee.org/document/876400

3D Multi-Subband Ensemble Monte Carlo Simulator of FinFETs and nanowire transistors

In this paper we present the development of a 3D Multi Subband Ensemble Monte Carlo (3DMSB-EMC) tool targeting the simulation of nanoscaled FinFETs and nanowire transistors. In order to deliver computational efficiency, we have developed a self-consistent framework that couples a MSB- EMC transport engine for a 1D electron gas with a 3DPoisson- 2DSchro ̈dinger solver. Here we use a FinFET with a physical channel length of 15nm as an example to demonstrate the appli- cability and highlight the benefits of the simulation framework. A comparison of the 3DMSB-EMC with Non-Equilibrium Green’s Functions (NEGFs) in the ballistic limit is used to verify and validate our approach

Static random-access memory designs based on different FinFET at lower technology node (7nm)

Title from PDF of title page viewed January 15, 2020Thesis advisor: Masud H ChowdhuryVitaIncludes bibliographical references (page 50-57)Thesis (M.S.)--School of Computing and Engineering. University of Missouri--Kansas City, 2019The Static Random-Access Memory (SRAM) has a significant performance impact on current nanoelectronics systems. To improve SRAM efficiency, it is important to utilize emerging technologies to overcome short-channel effects (SCE) of conventional CMOS. FinFET devices are promising emerging devices that can be utilized to improve the performance of SRAM designs at lower technology nodes. In this thesis, I present detail analysis of SRAM cells using different types of FinFET devices at 7nm technology. From the analysis, it can be concluded that the performance of both 6T and 8T SRAM designs are improved. 6T SRAM achieves a 44.97% improvement in the read energy compared to 8T SRAM. However, 6T SRAM write energy degraded by 3.16% compared to 8T SRAM. Read stability and write ability of SRAM cells are determined using Static Noise Margin and N- curve methods. Moreover, Monte Carlo simulations are performed on the SRAM cells to evaluate process variations. Simulations were done in HSPICE using 7nm Asymmetrical Underlap FinFET technology. The quasiplanar FinFET structure gained considerable attention because of the ease of the fabrication process [1] – [4]. Scaling of technology have degraded the performance of CMOS designs because of the short channel effects (SCEs) [5], [6]. Therefore, there has been upsurge in demand for FinFET devices for emerging market segments including artificial intelligence and cloud computing (AI) [8], [9], Internet of Things (IoT) [10] – [13] and biomedical [17] –[18] which have their own exclusive style of design. In recent years, many Underlapped FinFET devices were proposed to have better control of the SCEs in the sub-nanometer technologies [3], [4], [19] – [33]. Underlap on either side of the gate increases effective channel length as seen by the charge carriers. Consequently, the source-to-drain tunneling probability is improved. Moreover, edge direct tunneling leakage components can be reduced by controlling the electric field at the gate-drain junction . There is a limitation on the extent of underlap on drain or source sides because the ION is lower for larger underlap. Additionally, FinFET based designs have major width quantization issue. The width of a FinFET device increases only in quanta of silicon fin height (HFIN) [4]. The width quantization issue becomes critical for ratioed designs like SRAMs, where proper sizing of the transistors is essential for fault-free operation. FinFETs based on Design/Technology Co-Optimization (DTCO_F) approach can overcome these issues [38]. DTCO_F follows special design rules, which provides the specifications for the standard SRAM cells with special spacing rules and low leakages. The performances of 6T SRAM designs implemented by different FinFET devices are compared for different pull-up, pull down and pass gate transistor (PU: PD:PG) ratios to identify the best FinFET device for high speed and low power SRAM applications. Underlapped FinFETs (UF) and Design/Technology Co-Optimized FinFETs (DTCO_F) are used for the design and analysis. It is observed that with the PU: PD:PG ratios of 1:1:1 and 1:5:2 for the UF-SRAMs the read energy has degraded by 3.31% and 48.72% compared to the DTCO_F-SRAMs, respectively. However, the read energy with 2:5:2 ratio has improved by 32.71% in the UF-SRAM compared to the DTCO_F-SRAMs. The write energy with 1:1:1 configuration has improved by 642.27% in the UF-SRAM compared to the DTCO_F-SRAM. On the other hand, the write energy with 1:5:2 and 2:5:2 configurations have degraded by 86.26% and 96% in the UF-SRAMs compared to the DTCO_F-SRAMs. The stability and reliability of different SRAMs are also evaluated for 500mV supply. From the analysis, it can be concluded that Asymmetrical Underlapped FinFET is better for high-speed applications and DTCO FinFET for low power applications.Introduction -- Next generation high performance device: FinFET -- FinFET based SRAM bitcell designs -- Benchmarking of UF-SRAMs and DTCO-F-SRAMS -- Collaborative project -- Internship experience at INTEL and Marvell Semiconductor -- Conclusion and future wor

Design of High-Speed Dual Port 8T SRAM Cell with Simultaneous and Parallel READ-WRITE Feature

An innovative 8 transistor (8T) static random access memory (SRAM) architecture with a simple and reliable read operation is presented in this study. LTspice software is used to implement the suggested topology in the 16nm predictive technology model (PTM). Investigations into and comparisons with conventional 6T, 8T, 9T, and 10T SRAM cells have been made regarding read and write operations\u27 delay and power consumption as well as power delay product (PDP). The simulation outcomes show that the suggested design offers the fastest read operation and PDP optimization overall. Compared to the current 6T and 9T topologies, the noise margin is also enhanced. Finally, the comparison of the figure of merit (FoM) indicates the best efficiency of the proposed design

A Survey on Layout Implementation and Analysis of Different SRAM Cell Topologies

Because powered widgets are frequently used, the primary goal of electronics is to design low-power devices. Because of its applications in low-energy computing, memory cell operation with low voltage consumption has become a major interest in memory cell design. Because of specification changes in scaled methodologies, the only critical method for the success of low-voltage SRAM design is the stable operation of SRAM. The traditional SRAM cell enables high-density and fast differential sensing but suffers from semi-selective and read-risk issues. The simulation results show that the proposed design provides the fastest read operation and overall power delay product optimization. Compared to the current topologies of 6T, 8T, and 10T, while a traditional SRAM cell solves the reading disruption problem, previous strategies for solving these problems have been ineffective due to low efficiency, data-dependent leakage, and high energy per connection. Our primary goal is to reduce power consumption, improve read performance, and reduce the area and power of the proposed design cell work. The proposed leakage reduction design circuit has been implemented on the micro-wind tool. Delay and power consumption are important factors in memory cell performance. The primary goal of this project is to create a low-power SRAM cell