Investigating SRAM PUFs in large CPUs and GPUs

Physically unclonable functions (PUFs) provide data that can be used for cryptographic purposes: on the one hand randomness for the initialization of random-number generators; on the other hand individual fingerprints for unique identification of specific hardware components. However, today's off-the-shelf personal computers advertise randomness and individual fingerprints only in the form of additional or dedicated hardware. This paper introduces a new set of tools to investigate whether intrinsic PUFs can be found in PC components that are not advertised as containing PUFs. In particular, this paper investigates AMD64 CPU registers as potential PUF sources in the operating-system kernel, the bootloader, and the system BIOS; investigates the CPU cache in the early boot stages; and investigates shared memory on Nvidia GPUs. This investigation found non-random non-fingerprinting behavior in several components but revealed usable PUFs in Nvidia GPUs.Comment: 25 pages, 6 figures. Code in appendi

From 3D Models to 3D Prints: an Overview of the Processing Pipeline

Due to the wide diffusion of 3D printing technologies, geometric algorithms for Additive Manufacturing are being invented at an impressive speed. Each single step, in particular along the Process Planning pipeline, can now count on dozens of methods that prepare the 3D model for fabrication, while analysing and optimizing geometry and machine instructions for various objectives. This report provides a classification of this huge state of the art, and elicits the relation between each single algorithm and a list of desirable objectives during Process Planning. The objectives themselves are listed and discussed, along with possible needs for tradeoffs. Additive Manufacturing technologies are broadly categorized to explicitly relate classes of devices and supported features. Finally, this report offers an analysis of the state of the art while discussing open and challenging problems from both an academic and an industrial perspective.Comment: European Union (EU); Horizon 2020; H2020-FoF-2015; RIA - Research and Innovation action; Grant agreement N. 68044

What May Visualization Processes Optimize?

In this paper, we present an abstract model of visualization and inference processes and describe an information-theoretic measure for optimizing such processes. In order to obtain such an abstraction, we first examined six classes of workflows in data analysis and visualization, and identified four levels of typical visualization components, namely disseminative, observational, analytical and model-developmental visualization. We noticed a common phenomenon at different levels of visualization, that is, the transformation of data spaces (referred to as alphabets) usually corresponds to the reduction of maximal entropy along a workflow. Based on this observation, we establish an information-theoretic measure of cost-benefit ratio that may be used as a cost function for optimizing a data visualization process. To demonstrate the validity of this measure, we examined a number of successful visualization processes in the literature, and showed that the information-theoretic measure can mathematically explain the advantages of such processes over possible alternatives.Comment: 10 page