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The rapid improvement in computation capability has made deep convolutional neural networks (CNNs) a great success in recent years on many computer vision tasks with significantly improved accuracy. During the inference phase, many applications demand low latency processing of one image with strict power consumption requirement, which reduces the efficiency of GPU and other general-purpose platform, bringing opportunities for specific acceleration hardware, e.g. FPGA, by customizing the digital circuit specific for the deep learning algorithm inference. However, deploying CNNs on portable and embedded systems is still challenging due to large data volume, intensive computation, varying algorithm structures, and frequent memory …

Contributors

Ma, Yufei, Vrudhula, Sarma, Seo, Jae-sun, et al.

Created Date

2018

Semiconductor memory is a key component of the computing systems. Beyond the conventional memory and data storage applications, in this dissertation, both mainstream and eNVM memory technologies are explored for radiation environment, hardware security system and machine learning applications. In the radiation environment, e.g. aerospace, the memory devices face different energetic particles. The strike of these energetic particles can generate electron-hole pairs (directly or indirectly) as they pass through the semiconductor device, resulting in photo-induced current, and may change the memory state. First, the trend of radiation effects of the mainstream memory technologies with technology node scaling is reviewed. Then, …

Contributors

LIU, RUI, Yu, Shimeng, Yu, Shimeng, et al.

Created Date

2018

Static CMOS logic has remained the dominant design style of digital systems for more than four decades due to its robustness and near zero standby current. Static CMOS logic circuits consist of a network of combinational logic cells and clocked sequential elements, such as latches and flip-flops that are used for sequencing computations over time. The majority of the digital design techniques to reduce power, area, and leakage over the past four decades have focused almost entirely on optimizing the combinational logic. This work explores alternate architectures for the flip-flops for improving the overall circuit performance, power and area. It …

Contributors

Yang, Jinghua, Vrudhula, Sarma, Barnaby, Hugh, et al.

Created Date

2018

Scaling of the classical planar MOSFET below 20 nm gate length is facing not only technological difficulties but also limitations imposed by short channel effects, gate and junction leakage current due to quantum tunneling, high body doping induced threshold voltage variation, and carrier mobility degradation. Non-classical multiple-gate structures such as double-gate (DG) FinFETs and surrounding gate field-effect-transistors (SGFETs) have good electrostatic integrity and are an alternative to planar MOSFETs for below 20 nm technology nodes. Circuit design with these devices need compact models for SPICE simulation. In this work physics based compact models for the common-gate symmetric DG-FinFET, independent-gate asymmetric …

Contributors

Dessai, Gajanan, Gildenblat, Gennady, Gildenblat, Gennady, et al.

Created Date

2012

Lateral Double-diffused (LDMOS) transistors are commonly used in power management, high voltage/current, and RF circuits. Their characteristics include high breakdown voltage, low on-resistance, and compatibility with standard CMOS and BiCMOS manufacturing processes. As with other semiconductor devices, an accurate and physical compact model is critical for LDMOS-based circuit design. The goal of this research work is to advance the state-of-the-art by developing a physics-based scalable compact model of LDMOS transistors. The new model, SP-HV, is constructed from a surface-potential-based bulk MOSFET model, PSP, and a nonlinear resistor model, R3. The use of independently verified and mature sub-models leads to increased …

Contributors

Yao, Wei, Gildenblat, Gennady, Barnaby, Hugh, et al.

Created Date

2012