Implementation of JPEG compression and motion estimation on FPGA hardware

A hardware implementation of JPEG allows for real-time compression in data intensivve applications, such as high speed scanning, medical imaging and satellite image transmission. Implementation options include dedicated DSP or media processors, FPGA boards, and ASICs. Factors that affect the choice of platform selection involve cost, speed, memory, size, power consumption, and case of reconfiguration. The proposed hardware solution is based on a Very high speed integrated circuit Hardware Description Language (VHDL) implememtation of the codec with prefered realization using an FPGA board due to speed, cost and flexibility factors; The VHDL language is commonly used to model hardware impletations from a top down perspective. The VHDL code may be simulated to correct mistakes and subsequently synthesized into hardware using a synthesis tool, such as the xilinx ise suite. The same VHDL code may be synthesized into a number of sifferent hardware architetcures based on constraints given. For example speed was the major constraint when synthesizing the pipeline of jpeg encoding and decoding, while chip area and power consumption were primary constraints when synthesizing the on-die memory because of large area. Thus, there is a trade off between area and speed in logic synthesis

Advanced Information Processing Methods and Their Applications

This Special Issue has collected and presented breakthrough research on information processing methods and their applications. Particular attention is paid to the study of the mathematical foundations of information processing methods, quantum computing, artificial intelligence, digital image processing, and the use of information technologies in medicine

Integrated Circuits for Medical Ultrasound Applications: Imaging and Beyond

Medical ultrasound has become a crucial part of modern society and continues to play a vital role in the diagnosis and treatment of illnesses. Over the past decades, the develop- ment of medical ultrasound has seen extraordinary progress as a result of the tremendous research advances in microelectronics, transducer technology and signal processing algorithms. How- ever, medical ultrasound still faces many challenges including power-efficient driving of transducers, low-noise recording of ultrasound echoes, effective beamforming in a non-linear, high- attenuation medium (human tissues) and reduced overall form factor. This paper provides a comprehensive review of the design of integrated circuits for medical ultrasound applications. The most important and ubiquitous modules in a medical ultrasound system are addressed, i) transducer driving circuit, ii) low- noise amplifier, iii) beamforming circuit and iv) analog-digital converter. Within each ultrasound module, some representative research highlights are described followed by a comparison of the state-of-the-art. This paper concludes with a discussion and recommendations for future research directions

Design of Readout Electronics for the DEEP Particle Detector

Along with electromagnetic radiation, the Sun also emits a constant stream of charged particles in the form of solar wind. When these particles enter Earth’s atmosphere through a process known as particle precipitation, they can through a series of chemical reactions produce N Ox and HOx gases. These gases are greenhouse gases and deplete the ozone in the mesosphere and upper stratosphere. It is important to quantify the rate of production of these gases to model the potential climate impact. Existing particle detectors in space are suboptimal because they cannot determine the energy flux and pitch angle distribution of precipitating particles. The primary scientific objective of the DEEP project is to design a particle detector instrument that is specifically designed for particle precipitation measurements. This thesis investigates different data acquisition schemes for handling the signal from a pixel detector. The chosen approach is measuring the width of a shaped pulse to quantify the energy of the particle. Known as Time-over-Threshold, a detector circuit board is designed featuring high-speed comparators as threshold discriminators and the NG-MEDIUM FPGA from NanoXplore to implement the data acquisition. Digitizing the comparator pulse width is done with a Time-to-Digital converter (TDC) implemented in the FPGA fabric. Since the difference in pulse width is small for different energies, a high conversion resolution is required. Two high-resolution TDCs are designed and compared, both of which feature a digital counter and a method of interpolating the counter clock period. The first interpolation method applies the use of a multitapped delay line implemented with hard carry chain resources, and the second method oversamples the input with several equally off-phase sampling clocks. A resolution of 302 ps and a differential non-linearity of 3.26 was achieved with the delay line TDC clocked at 100 MHz. An automatic statistical calibration scheme is included to determine the actual delays of the delay line, utilizing a second asynchronous clock to generate uniformly distributed hits. The asynchronous oversampler resolution is clock frequency dependent and provides a 4-fold improvement to the clock period. The differential nonlinearity approaches zero with close matching of the off-phase clocks and operating frequency. A complete firmware design for the data acquisition and rocket telemetry of the detector is proposed and demonstrated. A simulation of the firmware utilizing each TDC topology is conducted and the delay line TDC is demonstrated to be the most accurate at all operating frequencies and thus the recommended TDC for the DEEP data acquisition.Masteroppgave i fysikkPHYS399MAMN-PHY

Low energy video processing and compression hardware designs

Digital video processing and compression algorithms are used in many commercial products such as mobile devices, unmanned aerial vehicles, and autonomous cars. Increasing resolution of videos used in these commercial products increased computational complexities of digital video processing and compression algorithms. Therefore, it is necessary to reduce computational complexities of digital video processing and compression algorithms, and energy consumptions of digital video processing and compression hardware without reducing visual quality. In this thesis, we propose a novel adaptive 2D digital image processing algorithm for 2D median filter, Gaussian blur and image sharpening. We designed low energy 2D median filter, Gaussian blur and image sharpening hardware using the proposed algorithm. We propose approximate HEVC intra prediction and HEVC fractional interpolation algorithms. We designed low energy approximate HEVC intra prediction and HEVC fractional interpolation hardware. We also propose several HEVC fractional interpolation hardware architectures. We propose novel computational complexity and energy reduction techniques for HEVC DCT and inverse DCT/DST. We designed high performance and low energy hardware for HEVC DCT and inverse DCT/DST including the proposed techniques. VII We quantified computation reductions achieved and video quality loss caused by the proposed algorithms and techniques. We implemented the proposed hardware architectures in Verilog HDL. We mapped the Verilog RTL codes to Xilinx Virtex 6 and Xilinx ZYNQ FPGAs, and estimated their power consumptions using Xilinx XPower Analyzer tool. The proposed algorithms and techniques significantly reduced the power and energy consumptions of these FPGA implementations in some cases with no PSNR loss and in some cases with very small PSNR loss

Energy and Area Efficient Machine Learning Architectures using Spin-Based Neurons

Recently, spintronic devices with low energy barrier nanomagnets such as spin orbit torque-Magnetic Tunnel Junctions (SOT-MTJs) and embedded magnetoresistive random access memory (MRAM) devices are being leveraged as a natural building block to provide probabilistic sigmoidal activation functions for RBMs. In this dissertation research, we use the Probabilistic Inference Network Simulator (PIN-Sim) to realize a circuit-level implementation of deep belief networks (DBNs) using memristive crossbars as weighted connections and embedded MRAM-based neurons as activation functions. Herein, a probabilistic interpolation recoder (PIR) circuit is developed for DBNs with probabilistic spin logic (p-bit)-based neurons to interpolate the probabilistic output of the neurons in the last hidden layer which are representing different output classes. Moreover, the impact of reducing the Magnetic Tunnel Junction\u27s (MTJ\u27s) energy barrier is assessed and optimized for the resulting stochasticity present in the learning system. In p-bit based DBNs, different defects such as variation of the nanomagnet thickness can undermine functionality by decreasing the fluctuation speed of the p-bit realized using a nanomagnet. A method is developed and refined to control the fluctuation frequency of the output of a p-bit device by employing a feedback mechanism. The feedback can alleviate this process variation sensitivity of p-bit based DBNs. This compact and low complexity method which is presented by introducing the self-compensating circuit can alleviate the influences of process variation in fabrication and practical implementation. Furthermore, this research presents an innovative image recognition technique for MNIST dataset on the basis of p-bit-based DBNs and TSK rule-based fuzzy systems. The proposed DBN-fuzzy system is introduced to benefit from low energy and area consumption of p-bit-based DBNs and high accuracy of TSK rule-based fuzzy systems. This system initially recognizes the top results through the p-bit-based DBN and then, the fuzzy system is employed to attain the top-1 recognition results from the obtained top outputs. Simulation results exhibit that a DBN-Fuzzy neural network not only has lower energy and area consumption than bigger DBN topologies while also achieving higher accuracy