Energy Saving Techniques for Phase Change Memory (PCM)

In recent years, the energy consumption of computing systems has increased and a large fraction of this energy is consumed in main memory. Towards this, researchers have proposed use of non-volatile memory, such as phase change memory (PCM), which has low read latency and power; and nearly zero leakage power. However, the write latency and power of PCM are very high and this, along with limited write endurance of PCM present significant challenges in enabling wide-spread adoption of PCM. To address this, several architecture-level techniques have been proposed. In this report, we review several techniques to manage power consumption of PCM. We also classify these techniques based on their characteristics to provide insights into them. The aim of this work is encourage researchers to propose even better techniques for improving energy efficiency of PCM based main memory.Comment: Survey, phase change RAM (PCRAM

Single particle 2D Electron crystallography for membrane protein structure determination

Proteins embedded into or attached to the cellular membrane perform crucial biological functions. Despite such importance, they remain among the most challenging targets of structural biology. Dedicated methods for membrane protein structure determination have been devised since decades, however with only partial success if compared to soluble proteins. One of these methods is 2D electron crystallography, in which the proteins are periodically arranged into a lipid bilayer. Using transmission electron microscopy to acquire projection images of samples containing such 2D crystals, which are embedded into a thin vitreous ice layer for radiation protection (cryo-EM), computer algorithms can be used to generate a 3D reconstruction of the protein. Unfortunately, in nearly every case, the 2D crystals are not flat and ordered enough to yield high-resolution reconstructions. Single particle analysis, on the other hand, is a technique that aligns projections of proteins isolated in solution in order to obtain a 3D reconstruction with a high success rate in terms of high resolution structures. In this thesis, we couple 2D crystal data processing with single particle analysis algorithms in order to perform a local correction of crystal distortions. We show that this approach not only allows reconstructions of much higher resolution than expected from the diffraction patterns obtained, but also reveals the existence of conformational heterogeneity within the 2D crystals. This structural variability can be linked to protein function, providing novel mechanistic insights and an explanation for why 2D crystals do not diffract to high resolution, in general. We present the computational methods that enable this hybrid approach, as well as other tools that aid several steps of cryo-EM data processing, from storage to postprocessing

ReSCon '11, Research Student Conference: Book of Abstracts

The fourth SED Research Student Conference (ReSCon2011) was hosted over three days, 20-22 June 2011, in the Hamilton Centre at Brunel University. The conference consisted of technical presentations, a poster session and social events. which focused on current research being conducted within the School of Engineering and Design by postgraduate research students from the School. The conference is held annually, and ReSCon plays a key role in contributing to research and innovations within the School

Advanced physical characterisation of milled pharmaceutical solids

Milling has been the key unit operation in controlling particle size of pharmaceutical powders at scale. The work carried out in this thesis is a comprehensive study of the stability of pharmaceutical solids post-milling and upon storage, from molecular level up to bulk handling scale. It is an attempt to fill key gaps in knowledge with regard to the anomalous behaviour and physical instability of milled powder through the development of advanced novel techniques. The physical instability of milled or amorphous pharmaceutical powders often manifest in changes in derived powder properties. Moisture induced dimensional changes of amorphous lactose compacts were monitored by in-situ environmental controlled optical profilometry. The complex volumetric behaviour involves glassy-rubbery phase transition followed by amorphous-crystalline transformation under the influence of water. These associated changes were not observed in physical aging of amorphous lactose compacts by measuring specific surface area. At the molecular level these physical changes are governed by relaxation processes. By operating within the linear viscoelastic region, low strain uni-axial indentation of small molecule organic glasses at a range of temperature generated master curves using WLF analysis. Viscoelastic behaviour of these materials were determined to be controlled by local β-relaxation around the glass transition rather than globally for polymers. At the bulk level, due to the non-equilibrium nature of milled and amorphous powders, their surface energies tends to be significantly higher than the equivalent crystalline forms. This can be detrimental as highly cohesive and poor flowing powders are difficult to process. The unconfined compression test was adapted to measure cohesion of small weak pharmaceutical powder compacts. More significantly, a positive relationship was confirmed between surface energetics and cohesion of modified D-mannitol. At the particle level, the mechanism(s) by which milling or micronisation creates low levels of amorphicity remains unclear. MOUDI fractionation of bulk micronised α-lactose monohydrate and characterisation of fine fractions has clearly demonstrated that micronisation as well as mechanical particle size reduction also generates low levels of highly amorphous ultrafine particles within bulk crystalline powder which will have a significant effect on powder physical stability post-milling and upon storage. In conclusion, using the novel techniques developed here, significant progress has been towards understanding the physical behaviour of milled and amorphous pharmaceutical solids

Algorithm and Hardware Co-design for Learning On-a-chip

abstract: Machine learning technology has made a lot of incredible achievements in recent years. It has rivalled or exceeded human performance in many intellectual tasks including image recognition, face detection and the Go game. Many machine learning algorithms require huge amount of computation such as in multiplication of large matrices. As silicon technology has scaled to sub-14nm regime, simply scaling down the device cannot provide enough speed-up any more. New device technologies and system architectures are needed to improve the computing capacity. Designing specific hardware for machine learning is highly in demand. Efforts need to be made on a joint design and optimization of both hardware and algorithm. For machine learning acceleration, traditional SRAM and DRAM based system suffer from low capacity, high latency, and high standby power. Instead, emerging memories, such as Phase Change Random Access Memory (PRAM), Spin-Transfer Torque Magnetic Random Access Memory (STT-MRAM), and Resistive Random Access Memory (RRAM), are promising candidates providing low standby power, high data density, fast access and excellent scalability. This dissertation proposes a hierarchical memory modeling framework and models PRAM and STT-MRAM in four different levels of abstraction. With the proposed models, various simulations are conducted to investigate the performance, optimization, variability, reliability, and scalability. Emerging memory devices such as RRAM can work as a 2-D crosspoint array to speed up the multiplication and accumulation in machine learning algorithms. This dissertation proposes a new parallel programming scheme to achieve in-memory learning with RRAM crosspoint array. The programming circuitry is designed and simulated in TSMC 65nm technology showing 900X speedup for the dictionary learning task compared to the CPU performance. From the algorithm perspective, inspired by the high accuracy and low power of the brain, this dissertation proposes a bio-plausible feedforward inhibition spiking neural network with Spike-Rate-Dependent-Plasticity (SRDP) learning rule. It achieves more than 95% accuracy on the MNIST dataset, which is comparable to the sparse coding algorithm, but requires far fewer number of computations. The role of inhibition in this network is systematically studied and shown to improve the hardware efficiency in learning.Dissertation/ThesisDoctoral Dissertation Electrical Engineering 201

Multidimensional Nonlinear Optical Imaging

The work in this dissertation is focused on extending the information content for second harmonic generation (SHG) and two-photon excited fluorescence (TPEF) imaging. Despite the simplicity and symmetry selectivity of nonlinear optical processes, limited information on chemical composition can be recovered solely based on intensity measurements. To further explore the potential for second order nonlinear optical (NLO) measurements, additional dimensions were added to the NLO imaging platforms. By combining NLO microscopy with powder X-ray diffraction, a novel approach was established for accessing percent crystallinity in amorphous solid dispersions (ASDs) with a limit of detection in the ppm range. ASDs are preferable alternative for crystalline forms when formulating poorly soluble active pharmaceutical ingredients (APIs). However, the high detection limit for current available methods limited the study of long term stability for ASDs at early stage. Besides adding additional modalities to NLO microscopy, polarization dependent SHG provides rich information on local structures for collagen fibers in tissues. However, significant loss in polarization purities occurs when light penetrate through the tissue. A new theoretical framework was introduced to extract information with partially or fully depolarized light. In addition, a video-rate hyperspectral TPEF imaging system was demonstrated with over 2,200 fluorescence channels throughput spatial-spectral multiplexing