Kamran Eshraghian

Bio: Kamran Eshraghian is an academic researcher from Chungbuk National University. The author has contributed to research in topics: Very-large-scale integration & Memristor. The author has an hindex of 24, co-authored 163 publications receiving 3482 citations. Previous affiliations of Kamran Eshraghian include University of Adelaide & Philips.

PRINCIPLES OF CMOS VLSI DESIGN A Systems Perspective Second Edition

Abstract: Introduction to CMOS Circuits. Introduction. MOS Transistors. MOS Transistor Switches. CMOS Logic. Circuit Representations. CMOS Summary. MOS Transistor Theory. Introduction. MOS Device Design Equation. The Complemenatry CMOS Inverter-DC Characteristics. Alternate CMOS Inverters. The Differential Stage. The Transmission Gate. Bipolar Devices. CMOS Processing Technology. Silicon Semiconductor Technology: An Overview. CMOS Technologies. Layout Design Rules. CAD Issues. Circuit Characterization and Performance Estimation. Introduction. Resistance Estimation. Capacitance Estimation. Inductance. Switching Characteristics. CMOS Gate Transistor Sizing. Power Consumption. Determination of Conductor Size. Charge Sharing. Design Margining. Yield. Scaling of MOS Transistor Dimensions. CMOS Circuit and Logic Design. Introduction. CMOS Logic Structures. Basic Physical Design of Simple Logic Gates. Clocking Strategies. Physical and Electrical Design of Logic Gates. 10 Structures. Structured Design Strategies. Introduction. Design Economics. Design Strategies. Design Methods. CMOS Chip Design Options. Design Capture Tools. Design Verification Tools. CMOS Test Methodolgies. Introduction. Fault Models. Design for Testability. Automatic Test Pattern Generation. Design for Manufacturability. CMOS Subsystem Design. Introduction. Adders and Related Functions. Binary Counters. Multipliers and Filter Structures. Random Access and Serial Memory. Datapaths. FIR and IIR Filters. Finite State Machines. Programmable Logic Arrays. Random Control Logic.

Memristor MOS Content Addressable Memory (MCAM): Hybrid Architecture for Future High Performance Search Engines

Abstract: Large-capacity content addressable memory (CAM) is a key element in a wide variety of applications. The inevitable complexities of scaling MOS transistors introduce a major challenge in the realization of such systems. Convergence of disparate technologies, which are compatible with CMOS processing, may allow extension of Moore's Law for a few more years. This paper provides a new approach towards the design and modeling of Memory resistor (Memristor)-based CAM (MCAM) using a combination of memristor MOS devices to form the core of a memory/compare logic cell that forms the building block of the CAM architecture. The non-volatile characteristic and the nanoscale geometry together with compatibility of the memristor with CMOS processing technology increases the packing density, provides for new approaches towards power management through disabling CAM blocks without loss of stored data, reduces power dissipation, and has scope for speed improvement as the technology matures.

Memristor MOS Content Addressable Memory (MCAM): Hybrid Architecture for Future High Performance Search Engines

Abstract: Large-capacity Content Addressable Memory (CAM) is a key element in a wide variety of applications. The inevitable complexities of scaling MOS transistors introduce a major challenge in the realization of such systems. Convergence of disparate technologies, which are compatible with CMOS processing, may allow extension of Moore's Law for a few more years. This paper provides a new approach towards the design and modeling of Memristor (Memory resistor) based Content Addressable Memory (MCAM) using a combination of memristor MOS devices to form the core of a memory/compare logic cell that forms the building block of the CAM architecture. The non-volatile characteristic and the nanoscale geometry together with compatibility of the memristor with CMOS processing technology increases the packing density, provides for new approaches towards power management through disabling CAM blocks without loss of stored data, reduces power dissipation, and has scope for speed improvement as the technology matures.

The fourth element: characteristics, modelling and electromagnetic theory of the memristor

Abstract: In 2008, researchers at the HewlettPackard (HP) laboratories published a paper in Nature reporting the development of a new basic circuit element that completes the missing link between charge and ...

Basic Vlsi Design

Abstract: A review of microelectronics and an introduction to MOS technology basic electrical properties of MOS and BiCMOS circuit design processes basic circuit concepts scaling of MOS circuits subsystem design and layout subsystems design processes illustration of the design process - computational elements memory, registers and aspects of system timing practical aspects and testability some CMOS design projects ultra fast VLSI circuits and systems - introduction to GaAs technology.

I and i

Abstract: There is, I think, something ethereal about i —the square root of minus one. I remember first hearing about it at school. It seemed an odd beast at that time—an intruder hovering on the edge of reality. Usually familiarity dulls this sense of the bizarre, but in the case of i it was the reverse: over the years the sense of its surreal nature intensified. It seemed that it was impossible to write mathematics that described the real world in …

Low-power CMOS digital design

Abstract: Motivated by emerging battery-operated applications that demand intensive computation in portable environments, techniques are investigated which reduce power consumption in CMOS digital circuits while maintaining computational throughput. Techniques for low-power operation are shown which use the lowest possible supply voltage coupled with architectural, logic style, circuit, and technology optimizations. An architecturally based scaling strategy is presented which indicates that the optimum voltage is much lower than that determined by other scaling considerations. This optimum is achieved by trading increased silicon area for reduced power consumption. >

Image Processing

Abstract: MUCKE aims to mine a large volume of images, to structure them conceptually and to use this conceptual structuring in order to improve large-scale image retrieval. The last decade witnessed important progress concerning low-level image representations. However, there are a number problems which need to be solved in order to unleash the full potential of image mining in applications. The central problem with low-level representations is the mismatch between them and the human interpretation of image content. This problem can be instantiated, for instance, by the incapability of existing descriptors to capture spatial relationships between the concepts represented or by their incapability to convey an explanation of why two images are similar in a content-based image retrieval framework. We start by assessing existing local descriptors for image classification and by proposing to use co-occurrence matrices to better capture spatial relationships in images. The main focus in MUCKE is on cleaning large scale Web image corpora and on proposing image representations which are closer to the human interpretation of images. Consequently, we introduce methods which tackle these two problems and compare results to state of the art methods. Note: some aspects of this deliverable are withheld at this time as they are pending review. Please contact the authors for a preview.

Silicon physical random functions

Abstract: We introduce the notion of a Physical Random Function (PUF). We argue that a complex integrated circuit can be viewed as a silicon PUF and describe a technique to identify and authenticate individual integrated circuits (ICs).We describe several possible circuit realizations of different PUFs. These circuits have been implemented in commodity Field Programmable Gate Arrays (FPGAs). We present experiments which indicate that reliable authentication of individual FPGAs can be performed even in the presence of significant environmental variations.We describe how secure smart cards can be built, and also briefly describe how PUFs can be applied to licensing and certification applications.

ISAAC: a convolutional neural network accelerator with in-situ analog arithmetic in crossbars

Abstract: A number of recent efforts have attempted to design accelerators for popular machine learning algorithms, such as those involving convolutional and deep neural networks (CNNs and DNNs). These algorithms typically involve a large number of multiply-accumulate (dot-product) operations. A recent project, DaDianNao, adopts a near data processing approach, where a specialized neural functional unit performs all the digital arithmetic operations and receives input weights from adjacent eDRAM banks.This work explores an in-situ processing approach, where memristor crossbar arrays not only store input weights, but are also used to perform dot-product operations in an analog manner. While the use of crossbar memory as an analog dot-product engine is well known, no prior work has designed or characterized a full-fledged accelerator based on crossbars. In particular, our work makes the following contributions: (i) We design a pipelined architecture, with some crossbars dedicated for each neural network layer, and eDRAM buffers that aggregate data between pipeline stages. (ii) We define new data encoding techniques that are amenable to analog computations and that can reduce the high overheads of analog-to-digital conversion (ADC). (iii) We define the many supporting digital components required in an analog CNN accelerator and carry out a design space exploration to identify the best balance of memristor storage/compute, ADCs, and eDRAM storage on a chip. On a suite of CNN and DNN workloads, the proposed ISAAC architecture yields improvements of 14.8×, 5.5×, and 7.5× in throughput, energy, and computational density (respectively), relative to the state-of-the-art DaDianNao architecture.