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

NEW MATERIAL FOR ELIMINATING LINEAR ENERGY TRANSFER SENSITIVITIES IN DEEPLY SCALED CMOS TECHNOLOGIES SRAM CELLS

As technology scales deep in submicron regime, CMOS SRAM memories have become increasingly sensitive to Single-Event Upset sensitivity. Key technological factors that impact Single-Event Upset sensitivity are gate length, gate and drain areas and the power supply voltage all of which impact transistor's nodal capacitance. In this work, I present engineering requirement studies, which show for the first time, the tread of Single-Event Upset sensitivity in deeply scaled SRAM cells. To mitigate the Single-Event Upset sensitivity, a novel approach is presented, illustrating exactly how material defects can be managed in a way that sets electrical resistance of the material as desired. A thin-film high-resistance value ranging from 2kΩ/-3.6MΩ/, and TCR of negative 0.0016%/˚C is presented. A defect model is presented that agrees well with the experimental results. These resistors are used in the cross-coupled latches; to decouple the latch nodes and delay the regenerative action of the cell, thus hardening against single even upset (SEU)

Digital and analog TFET circuits: Design and benchmark

In this work, we investigate by means of simulations the performance of basic digital, analog, and mixed-signal circuits employing tunnel-FETs (TFETs). The analysis reviews and complements our previous papers on these topics. By considering the same devices for all the analysis, we are able to draw consistent conclusions for a wide variety of circuits. A virtual complementary TFET technology consisting of III-V heterojunction nanowires is considered. Technology Computer Aided Design (TCAD) models are calibrated against the results of advanced full-quantum simulation tools and then used to generate look-up-tables suited for circuit simulations. The virtual complementary TFET technology is benchmarked against predictive technology models (PTM) of complementary silicon FinFETs for the 10 nm node over a wide range of supply voltages (VDD) in the sub-threshold voltage domain considering the same footprint between the vertical TFETs and the lateral FinFETs and the same static power. In spite of the asymmetry between p- and n-type transistors, the results show clear advantages of TFET technology over FinFET for VDDlower than 0.4 V. Moreover, we highlight how differences in the I-V characteristics of FinFETs and TFETs suggest to adapt the circuit topologies used to implement basic digital and analog blocks with respect to the most common CMOS solutions

MATIC: Learning Around Errors for Efficient Low-Voltage Neural Network Accelerators

As a result of the increasing demand for deep neural network (DNN)-based services, efforts to develop dedicated hardware accelerators for DNNs are growing rapidly. However,while accelerators with high performance and efficiency on convolutional deep neural networks (Conv-DNNs) have been developed, less progress has been made with regards to fully-connected DNNs (FC-DNNs). In this paper, we propose MATIC (Memory Adaptive Training with In-situ Canaries), a methodology that enables aggressive voltage scaling of accelerator weight memories to improve the energy-efficiency of DNN accelerators. To enable accurate operation with voltage overscaling, MATIC combines the characteristics of destructive SRAM reads with the error resilience of neural networks in a memory-adaptive training process. Furthermore, PVT-related voltage margins are eliminated using bit-cells from synaptic weights as in-situ canaries to track runtime environmental variation. Demonstrated on a low-power DNN accelerator that we fabricate in 65 nm CMOS, MATIC enables up to 60-80 mV of voltage overscaling (3.3x total energy reduction versus the nominal voltage), or 18.6x application error reduction.Comment: 6 pages, 12 figures, 3 tables. Published at Design, Automation and Test in Europe Conference and Exhibition (DATE) 201