The MUSE Machine -- an Architecture for Structured Data Flow Computation

Computers employing some degree of data flow organisation are now well established as providing a possible vehicle for concurrent computation. Although data-driven computation frees the architecture from the constraints of the single program counter, processor and global memory, inherent in the classic von Neumann computer, there can still be problems with the unconstrained generation of fresh result tokens if a pure data flow approach is adopted. The advantages of allowing serial processing for those parts of a program which are inherently serial, and of permitting a demand-driven, as well as data-driven, mode of operation are identified and described. The MUSE machine described here is a structured architecture supporting both serial and parallel processing which allows the abstract structure of a program to be mapped onto the machine in a logical way.

Decomposable approximations and coloured isomorphisms for C*-algebras

In this thesis we introduce nuclear dimension and compare it with a stronger form of the completely positive approximation property. We show that the approximations forming this stronger characterisation of the completely positive approximation property witness finite nuclear dimension if and only if the underlying C*-algebra is approximately finite dimensional. We also extend this result to nuclear dimension at most omega. We review interactions between separably acting injective von Neumann algebras and separable nuclear C*-algebras. In particular, we discuss aspects of Connes' work and how some of his strategies have been used by C^*-algebraist to estimate the nuclear dimension of certain classes of C*-algebras. We introduce a notion of coloured isomorphisms between separable unital C*-algebras. Under these coloured isomorphisms ideal lattices, trace spaces, commutativity, nuclearity, finite nuclear dimension and weakly pure infiniteness are preserved. We show that these coloured isomorphisms induce isomorphisms on the classes of finite dimensional and commutative C*-algebras. We prove that any pair of Kirchberg algebras are 2-coloured isomorphic and any pair of separable, simple, unital, finite, nuclear and Z-stable C*-algebras with unique trace which satisfy the UCT are also 2-coloured isomorphic.

Edge centrality via the Holevo quantity

In the study of complex networks, vertex centrality measures are used to identify the most important vertices within a graph. A related problem is that of measuring the centrality of an edge. In this paper, we propose a novel edge centrality index rooted in quantum information. More specifically, we measure the importance of an edge in terms of the contribution that it gives to the Von Neumann entropy of the graph. We show that this can be computed in terms of the Holevo quantity, a well known quantum information theoretical measure. While computing the Von Neumann entropy and hence the Holevo quantity requires computing the spectrum of the graph Laplacian, we show how to obtain a simplified measure through a quadratic approximation of the Shannon entropy. This in turns shows that the proposed centrality measure is strongly correlated with the negative degree centrality on the line graph. We evaluate our centrality measure through an extensive set of experiments on real-world as well as synthetic networks, and we compare it against commonly used alternative measures.

Flexible Computing Systems For AI Acceleration At The Extreme Edge Of The IoT

Embedding intelligence in extreme edge devices allows distilling raw data acquired from sensors into actionable information, directly on IoT end-nodes. This computing paradigm, in which end-nodes no longer depend entirely on the Cloud, offers undeniable benefits, driving a large research area (TinyML) to deploy leading Machine Learning (ML) algorithms on micro-controller class of devices. To fit the limited memory storage capability of these tiny platforms, full-precision Deep Neural Networks (DNNs) are compressed by representing their data down to byte and sub-byte formats, in the integer domain. However, the current generation of micro-controller systems can barely cope with the computing requirements of QNNs. This thesis tackles the challenge from many perspectives, presenting solutions both at software and hardware levels, exploiting parallelism, heterogeneity and software programmability to guarantee high flexibility and high energy-performance proportionality. The first contribution, PULP-NN, is an optimized software computing library for QNN inference on parallel ultra-low-power (PULP) clusters of RISC-V processors, showing one order of magnitude improvements in performance and energy efficiency, compared to current State-of-the-Art (SoA) STM32 micro-controller systems (MCUs) based on ARM Cortex-M cores. The second contribution is XpulpNN, a set of RISC-V domain specific instruction set architecture (ISA) extensions to deal with sub-byte integer arithmetic computation. The solution, including the ISA extensions and the micro-architecture to support them, achieves energy efficiency comparable with dedicated DNN accelerators and surpasses the efficiency of SoA ARM Cortex-M based MCUs, such as the low-end STM32M4 and the high-end STM32H7 devices, by up to three orders of magnitude. To overcome the Von Neumann bottleneck while guaranteeing the highest flexibility, the final contribution integrates an Analog In-Memory Computing accelerator into the PULP cluster, creating a fully programmable heterogeneous fabric that demonstrates end-to-end inference capabilities of SoA MobileNetV2 models, showing two orders of magnitude performance improvements over current SoA analog/digital solutions.

PCM-based in-memory computing: architectures, circuits and applications

Analog In-memory Computing (AIMC) has been proposed in the context of Beyond Von Neumann architectures as a valid strategy to reduce internal data transfers energy consumption and latency, and to improve compute efficiency. The aim of AIMC is to perform computations within the memory unit, typically leveraging the physical features of memory devices. Among resistive Non-volatile Memories (NVMs), Phase-change Memory (PCM) has become a promising technology due to its intrinsic capability to store multilevel data. Hence, PCM technology is currently investigated to enhance the possibilities and the applications of AIMC. This thesis aims at exploring the potential of new PCM-based architectures as in-memory computational accelerators. In a first step, a preliminar experimental characterization of PCM devices has been carried out in an AIMC perspective. PCM cells non-idealities, such as time-drift, noise, and non-linearity have been studied to develop a dedicated multilevel programming algorithm. Measurement-based simulations have been then employed to evaluate the feasibility of PCM-based operations in the fields of Deep Neural Networks (DNNs) and Structural Health Monitoring (SHM). Moreover, a first testchip has been designed and tested to evaluate the hardware implementation of Multiply-and-Accumulate (MAC) operations employing PCM cells. This prototype experimentally demonstrates the possibility to reach a 95% MAC accuracy with a circuit-level compensation of cells time drift and non-linearity. Finally, empirical circuit behavior models have been included in simulations to assess the use of this technology in specific DNN applications, and to enhance the potentiality of this innovative computation approach.

Quantum information and quantum communication theory in relation to black hole idealized mechanical models

Oggigiorno il concetto di informazione è diventato cruciale in fisica, pertanto, siccome la migliore teoria che abbiamo per compiere predizioni riguardo l'universo è la meccanica quantistica, assume una particolare importanza lo sviluppo di una versione quantistica della teoria dell'informazione. Questa centralità è confermata dal fatto che i buchi neri hanno entropia. Per questo motivo, in questo lavoro sono presentati elementi di teoria dell'informazione quantistica e della comunicazione quantistica e alcuni sono illustrati riferendosi a modelli quantistici altamente idealizzati della meccanica di buco nero. In particolare, nel primo capitolo sono forniti tutti gli strumenti quanto-meccanici per la teoria dell'informazione e della comunicazione quantistica. Successivamente, viene affrontata la teoria dell'informazione quantistica e viene trovato il limite di Bekenstein alla quantità di informazione chiudibile entro una qualunque regione spaziale. Tale questione viene trattata utilizzando un modello quantistico idealizzato della meccanica di buco nero supportato dalla termodinamica. Nell'ultimo capitolo, viene esaminato il problema di trovare un tasso raggiungibile per la comunicazione quantistica facendo nuovamente uso di un modello quantistico idealizzato di un buco nero, al fine di illustrare elementi della teoria. Infine, un breve sommario della fisica dei buchi neri è fornito in appendice.

Die Constäpler bleiben; kühl' Wetter bleibt! Nu noch de Cholera, denn is de Pulle voll! Neumann: Na die jestrige Sitzung von de Vertretersch in de Singe-Akademie is nich bitter jewesen ...

Welsch (Projektbearbeiter): Kritische Kommentierung der in der Preußischen Nationalversammlung am 9. August 1848 geführten Debatte über die Berliner Konstabler-Schutztruppe. In der Form einer Unterhaltung zwischen den Berliner Bürgern Neumann, Piesecke und Brennecke

Beiträge zum studium der randwertaufgaben, von C. Neumann ... Mit 59 figuren im text.

Mode of access: Internet.

Aus der Festungszeit preussischer Kammergerichts- und Regierungsräte auf Spandau, 1780; Tagebuch des Regierungsrates Neumann, geschrieben während der von Friedrich dem Grossen über die Richter im Müller Arnold-Prozess verhängten Festungshaft.

Mode of access: Internet.

Sequence variations in the von Hippel-Lindau tumor suppressor gene in patients with intracranial aneurysms

The rupture of intracranial aneurysms leads to subarachnoid hemorrhage, which is often associated with poor outcome. Preventive treatment of unruptured intracranial aneurysms is possible and recommended. However, the lack of candidate genes precludes identifying patients at risk by genetic analyses. We observed intracranial aneurysms in 2 patients with von Hippel-Lindau (VHL) disease and the known disease-causing mutation c.292T > C (p.Tyr98His) in the VHL tumor suppressor gene. This study investigates whether the VHL gene is a possible candidate gene for aneurysm formation.