Efficient modular arithmetic units for low power cryptographic applications

The demand for high security in energy constrained devices such as mobiles and PDAs is growing rapidly. This leads to the need for efficient design of cryptographic algorithms which offer data integrity, authentication, non-repudiation and confidentiality of the encrypted data and communication channels. The public key cryptography is an ideal choice for data integrity, authentication and non-repudiation whereas the private key cryptography ensures the confidentiality of the data transmitted. The latter has an extremely high encryption speed but it has certain limitations which make it unsuitable for use in certain applications. Numerous public key cryptographic algorithms are available in the literature which comprise modular arithmetic modules such as modular addition, multiplication, inversion and exponentiation. Recently, numerous cryptographic algorithms have been proposed based on modular arithmetic which are scalable, do word based operations and efficient in various aspects. The modular arithmetic modules play a crucial role in the overall performance of the cryptographic processor. Hence, better results can be obtained by designing efficient arithmetic modules such as modular addition, multiplication, exponentiation and squaring. This thesis is organized into three papers, describes the efficient implementation of modular arithmetic units, application of these modules in International Data Encryption Algorithm (IDEA). Second paper describes the IDEA algorithm implementation using the existing techniques and using the proposed efficient modular units. The third paper describes the fault tolerant design of a modular unit which has online self-checking capability --Abstract, page iv

XBioSiP: A Methodology for Approximate Bio-Signal Processing at the Edge

Bio-signals exhibit high redundancy, and the algorithms for their processing are inherently error resilient. This property can be leveraged to improve the energy-efficiency of IoT-Edge (wearables) through the emerging trend of approximate computing. This paper presents XBioSiP, a novel methodology for approximate bio-signal processing that employs two quality evaluation stages, during the pre-processing and bio-signal processing stages, to determine the approximation parameters. It thereby achieves high energy savings while satisfying the user-determined quality constraint. Our methodology achieves, up to 19x and 22x reduction in the energy consumption of a QRS peak detection algorithm for 0% and <1% loss in peak detection accuracy, respectively.Comment: Accepted for publication at the Design Automation Conference 2019 (DAC'19), Las Vegas, Nevada, US

An efficient multiple precision floating-point Multiply-Add Fused unit

Multiply-Add Fused (MAF) units play a key role in the processor's performance for a variety of applications. The objective of this paper is to present a multi-functional, multiple precision floating-point Multiply-Add Fused (MAF) unit. The proposed MAF is reconfigurable and able to execute a quadruple precision MAF instruction, or two double precision instructions, or four single precision instructions in parallel. The MAF architecture features a dual-path organization reducing the latency of the floating-point add (FADD) instruction and utilizes the minimum number of operating components to keep the area low. The proposed MAF design was implemented on a 65 nm silicon process achieving a maximum operating frequency of 293.5 MHz at 381 mW power

Automatic rapid prototyping of semi-custom VLSI circuits using FPGAs

Journal ArticleWe describe a technique for translating semi-custom VLSI circuits automatically, integrating two design environments, into field programmable gate arrays (FPGAs) for rapid and inexpensive prototyping. The VLSI circuits are designed using a cell-matrix based environment that produces chips with density comparable to full custom VLSI design. These circuits are translated automatically into FPGAs for testing and system development. A four-bit pipelined array multiplier is used as an example of this translation. The multiplier is implemented in CMOS in both synchronous and asynchronous pipelined versions, and translated into Actel FPGAs both automatically, and by hand for comparison. The six test chips were all found to be fully functional, and the translation efficiency in terms of chip speed and area is shown. This result demonstrates the potential of this approach to system development

APPROXIMATE COMPUTING BASED PROCESSING OF MEA SIGNALS ON FPGA

The Microelectrode Array (MEA) is a collection of parallel electrodes that may measure the extracellular potential of nearby neurons. It is a crucial tool in neuroscience for researching the structure, operation, and behavior of neural networks. Using sophisticated signal processing techniques and architectural templates, the task of processing and evaluating the data streams obtained from MEAs is a computationally demanding one that needs time and parallel processing.This thesis proposes enhancing the capability of MEA signal processing systems by using approximate computing-based algorithms. These algorithms can be implemented in systems that process parallel MEA channels using the Field Programmable Gate Arrays (FPGAs). In order to develop approximate signal processing algorithms, three different types of approximate adders are investigated in various configurations. The objective is to maximize performance improvements in terms of area, power consumption, and latency associated with real-time processing while accepting lower output accuracy within certain bounds. On FPGAs, the methods are utilized to construct approximate processing systems, which are then contrasted with the precise system. Real biological signals are used to evaluate both precise and approximative systems, and the findings reveal notable improvements, especially in terms of speed and area. Processing speed enhancements reach up to 37.6%, and area enhancements reach 14.3% in some approximate system modes without sacrificing accuracy. Additional cases demonstrate how accuracy, area, and processing speed may be traded off. Using approximate computing algorithms allows for the design of real-time MEA processing systems with higher speeds and more parallel channels. The application of approximate computing algorithms to process biological signals on FPGAs in this thesis is a novel idea that has not been explored before

A Novel Reconfiguration Scheme in Quantum-Dot Cellular Automata for Energy Efficient Nanocomputing

Quantum-Dot Cellular Automata (QCA) is currently being investigated as an alternative to CMOS technology. There has been extensive study on a wide range of circuits from simple logical circuits such as adders to complex circuits such as 4-bit processors. At the same time, little if any work has been done in considering the possibility of reconfiguration to reduce power in QCA devices. This work presents one of the first such efforts when considering reconfigurable QCA architectures which are expected to be both robust and power efficient. We present a new reconfiguration scheme which is highly robust and is expected to dissipate less power with respect to conventional designs. An adder design based on the reconfiguration scheme will be presented in this thesis, with a detailed power analysis and comparison with existing designs. In order to overcome the problems of routing which comes with reconfigurability, a new wire crossing mechanism is also presented as part of this thesis