More Like this

1. Direct Digital Subtractive Manufacturing of Functional Assemblies Using Voxel-Based Models

   144. Lynn, R. ( November 2017 , Journal of manufacturing science and engineering)

   Direct digital manufacturing (DDM) is the creation of a physical part directly from a computer-aided design (CAD) model with minimal process planning and is typically applied to additive manufacturing (AM) processes to fabricate complex geometry. AM is preferred for DDM because of its minimal user input requirements; as a result, users can focus on exploiting other advantages of AM, such as the creation of intricate mechanisms that require no assembly after fabrication. Such assembly free mechanisms can be created using DDM during a single build process. In contrast, subtractive manufacturing (SM) enables the creation of higher strength parts that do not suffer from the material anisotropy inherent in AM. However, process planning for SM is more difficult than it is for AM due to geometric constraints imposed by the machining process; thus, the application of SM to the fabrication of assembly free mechanisms is challenging. This research describes a voxel-based computer-aided manufacturing (CAM) system that enables direct digital subtractive manufacturing (DDSM) of an assembly free mechanism. Process planning for SM involves voxel-by-voxel removal of material in the same way that an AM process consists of layer-by-layer addition of material. The voxelized CAM system minimizes user input by automatically generating toolpaths based on an analysis of accessible material to remove for a certain clearance in the mechanism's assembled state. The DDSM process is validated and compared to AM using case studies of the manufacture of two assembly free ball-in-socket mechanisms. 

   more » « less
2. Multi-Axis Voxel-Based CNC Machining of Centrifugal Compressor Assemblies

   Kurfess, T.R. ( May 2018 , American Helicopter Society Forum 74)

   The use of computer-aided manufacturing (CAM) software is essential in the rapid production of high-quality computer numerical control (CNC) machining toolpaths for complex parts. Typical CAM software relies on analytical representations of part geometry, where curves and surfaces are described by parametric functions. This paper proposes the use of a novel way to represent part geometry known as a voxel model. A voxel model uses a three-dimensional array of small cubes to represent a part volume; these cubes, or voxels, are the three-dimensional analog of two-dimensional pixels in an image. The use of voxels for a CAM application enables higher surface complexity, simplified collision checking, and more robust analysis of material removal than would be possible with typical parametric CAM. The unique capabilities of the voxel-based CAM approach described in this paper enable rapid production of high-quality 5-axis toolpaths for machining complex parts, such as the centrifugal compressor assembly that is presented in this work. 

   more » « less
3. QuanTaichi: a compiler for quantized simulations

   https://doi.org/10.1145/3450626.3459671  

   Hu, Yuanming ; Liu, Jiafeng ; Yang, Xuanda ; Xu, Mingkuan ; Kuang, Ye ; Xu, Weiwei ; Dai, Qiang ; Freeman, William T. ; Durand, Frédo ( August 2021 , ACM Transactions on Graphics)

   High-resolution simulations can deliver great visual quality, but they are often limited by available memory, especially on GPUs. We present a compiler for physical simulation that can achieve both high performance and significantly reduced memory costs, by enabling flexible and aggressive quantization. Low-precision ("quantized") numerical data types are used and packed to represent simulation states, leading to reduced memory space and bandwidth consumption. Quantized simulation allows higher resolution simulation with less memory, which is especially attractive on GPUs. Implementing a quantized simulator that has high performance and packs the data tightly for aggressive storage reduction would be extremely labor-intensive and error-prone using a traditional programming language. To make the creation of quantized simulation practical, we have developed a new set of language abstractions and a compilation system. A suite of tailored domain-specific optimizations ensure quantized simulators often run as fast as the full-precision simulators, despite the overhead of encoding-decoding the packed quantized data types. Our programming language and compiler, based on Taichi , allow developers to effortlessly switch between different full-precision and quantized simulators, to explore the full design space of quantization schemes, and ultimately to achieve a good balance between space and precision. The creation of quantized simulation with our system has large benefits in terms of memory consumption and performance, on a variety of hardware, from mobile devices to workstations with high-end GPUs. We can simulate with levels of resolution that were previously only achievable on systems with much more memory, such as multiple GPUs. For example, on a single GPU, we can simulate a Game of Life with 20 billion cells (8× compression per pixel), an Eulerian fluid system with 421 million active voxels (1.6× compression per voxel), and a hybrid Eulerian-Lagrangian elastic object simulation with 235 million particles (1.7× compression per particle). At the same time, quantized simulations create physically plausible results. Our quantization techniques are complementary to existing acceleration approaches of physical simulation: they can be used in combination with these existing approaches, such as sparse data structures, for even higher scalability and performance. 

   more » « less
4. FireMarshal: Making HW/SW Co-Design Reproducible and Reliable

   https://doi.org/10.1109/ISPASS51385.2021.00052  

   Pemberton, Nathan ; Amid, Alon ( March 2021 , 2021 IEEE International Symposium on Performance Analysis of Systems and Software (ISPASS))

   null (Ed.)

   Reproducibility in the sciences is critical to reliable inquiry, but is often easier said than done. In the computer architecture community, research may require modifying systems from low-level circuits to operating systems and highlevel applications. All of these moving parts make reproducible experiments on full-stack systems challenging to design. Furthermore, the computing ecosystem evolves quickly, leading to rapidly obsolete artifacts. This is especially true in the realm of software where applications are often updated on a monthly, or even daily, cadence. In this paper we introduce FireMarshal, a software workload management tool for RISC-V based full-stack hardware development and research. FireMarshal automates workload generation (constructing boot binaries and filesystem images), development (with functional simulation), and evaluation (with cycle-exact RTL simulation). It also ensures, to the extent possible, that the exact same software runs deterministically across all phases of development, providing confidence in correctness and accuracy while minimizing time spent on slow and expensive RTL-level simulation. To ease workload specification, FireMarshal provides sane defaults for common components like firmware and operating systems, freeing users to focus only on project-specific components. Beyond reproducibility, Fire- Marshal enables continued development of workloads through the use of inheritance, where new workloads can be derived from established and continually updated base workloads. Users communicate their designs through the use of simple JSON configuration files that can be easily version controlled, reused, and shared. In this paper, we describe the design of FireMarshal along with the associated software management methodology for architectural research and development. 

   more » « less
5. Hardening the Security of Multi-Access Edge Computing through Bio-Inspired VM Introspection

   https://doi.org/10.3390/bdcc5040052  

   Huseynov H., Saadawi T. ( October 2021 , Big data and cognitive computing)

   null (Ed.)

   The extreme bandwidth and performance of 5G mobile networks changes the way we develop and utilize digital services. Within a few years, 5G will not only touch technology and applications, but dramatically change the economy, our society and individual life. One of the emerging technologies that enables the evolution to 5G by bringing cloud capabilities near to the end users is Edge Computing or also known as Multi-Access Edge Computing (MEC) that will become pertinent towards the evolution of 5G. This evolution also entails growth in the threat landscape and increase privacy in concerns at different application areas, hence security and privacy plays a central role in the evolution towards 5G. Since MEC application instantiated in the virtualized infrastructure, in this paper we present a distributed application that aims to constantly introspect multiple virtual machines (VMs) in order to detect malicious activities based on their anomalous behavior. Once suspicious processes detected, our IDS in real-time notifies system administrator about the potential threat. Developed software is able to detect keyloggers, rootkits, trojans, process hiding and other intrusion artifacts via agent-less operation, by operating remotely or directly from the host machine. Remote memory introspection means no software to install, no notice to malware to evacuate or destroy data. Experimental results of remote VMI on more than 50 different malicious code demonstrate average anomaly detection rate close to 97%. We have established wide testbed environment connecting networks of two universities Kyushu Institute of Technology and The City College of New York through secure GRE tunnel. Conducted experiments on this testbed deliver high response time of the proposed system. 

   more » « less