Evgenii Rezunov, Niko Zurstraßen, Lennart M. Reimann
et al.
An Application-Specific Instruction Set Processor(ASIP) is a specialized microprocessor that provides a trade-off between the programmability of a General Purpose Processor (GPP) and the performance and energy-efficiency of dedicated hardware accelerators. ASIPs are often derived from off-the-shelf GPPs extended by custom instructions tailored towards a specific software workload. One of the most important challenges of designing an ASIP is to find said custom instructions that help to increase performance without being too costly in terms of area and power consumption. To date, solving this challenge is relatively labor-intensive and typically performed manually. Addressing the lack of automation, we present Custom Instruction Designer for RISC-V Extensions (CIDRE), a front-to-back tool for ASIP design. CIDRE automatically analyzes hotspots in RISC-V applications and generates custom instruction suggestions with a corresponding nML description. The nML description can be used with other electronic design automation tools to accurately assess the cost and benefits of the found suggestions. In a RISC-V benchmark study, we were able to accelerate embedded benchmarks from Embench and MiBench by up to 2.47x with less than 24% area increase. The entire process was conducted completely automatically.
Florentia Afentaki, Maha Shatta, Konstantinos Balaskas
et al.
Flexible Electronics (FE) have emerged as a promising alternative to silicon-based technologies, offering on-demand low-cost fabrication, conformality, and sustainability. However, their large feature sizes severely limit integration density, imposing strict area and power constraints, thus prohibiting the realization of Machine Learning (ML) circuits, which can significantly enhance the capabilities of relevant near-sensor applications. Support Vector Machines (SVMs) offer high accuracy in such applications at relatively low computational complexity, satisfying FE technologies' constraints. Existing SVM designs rely solely on linear or Radial Basis Function (RBF) kernels, forcing a trade-off between hardware costs and accuracy. Linear kernels, implemented digitally, minimize overhead but sacrifice performance, while the more accurate RBF kernels are prohibitively large in digital, and their analog realization contains inherent functional approximation. In this work, we propose the first mixed-kernel and mixed-signal SVM design in FE, which unifies the advantages of both implementations and balances the cost/accuracy trade-off. To that end, we introduce a co-optimization approach that trains our mixed-kernel SVMs and maps binary SVM classifiers to the appropriate kernel (linear/RBF) and domain (digital/analog), aiming to maximize accuracy whilst reducing the number of costly RBF classifiers. Our designs deliver 7.7% higher accuracy than state-of-the-art single-kernel linear SVMs, and reduce area and power by 108x and 17x on average compared to digital RBF implementations.
Video Detailed Captioning (VDC) is a crucial task for vision-language bridging, enabling fine-grained descriptions of complex video content. In this paper, we first comprehensively benchmark current state-of-the-art approaches and systematically identified two critical limitations: biased capability towards specific captioning aspect and misalignment with human preferences. To address these deficiencies, we propose Cockatiel, a novel three-stage training pipeline that ensembles synthetic and human-aligned training for improving VDC performance. In the first stage, we derive a scorer from a meticulously annotated dataset to select synthetic captions high-performing on certain fine-grained video-caption alignment and human-preferred while disregarding others. Then, we train Cockatiel-13B, using this curated dataset to infuse it with assembled model strengths and human preferences. Finally, we further distill Cockatiel-8B from Cockatiel-13B for the ease of usage. Extensive quantitative and qualitative experiments reflect the effectiveness of our method, as we not only set new state-of-the-art performance on VDCSCORE in a dimension-balanced way but also surpass leading alternatives on human preference by a large margin as depicted by the human evaluation results.
Battery-powered IoT devices face challenges like cost, maintenance, and environmental sustainability, prompting the emergence of batteryless energy-harvesting systems that harness ambient sources. However, their intermittent behavior can disrupt program execution and cause data loss, leading to unpredictable outcomes. Despite exhaustive studies employing conventional checkpoint methods and intricate programming paradigms to address these pitfalls, this paper proposes an innovative systematic methodology, namely DIAC. The DIAC synthesis procedure enhances the performance and efficiency of intermittent computing systems, with a focus on maximizing forward progress and minimizing the energy overhead imposed by distinct memory arrays for backup. Then, a finite-state machine is delineated, encapsulating the core operations of an IoT node, sense, compute, transmit, and sleep states. First, we validate the robustness and functionalities of a DIAC-based design in the presence of power disruptions. DIAC is then applied to a wide range of benchmarks, including ISCAS-89, MCNS, and ITC-99. The simulation results substantiate the power-delay-product (PDP) benefits. For example, results for complex MCNC benchmarks indicate a PDP improvement of 61%, 56%, and 38% on average compared to three alternative techniques, evaluated at 45 nm.
Joshua Prendergast, Manaswin Oddiraju, Mostafa Nouh
et al.
Auxetics refer to a class of engineered structures which exhibit an overall negative Poisson's ratio. These structures open up various potential opportunities in impact resistance, high energy absorption, and flexible robotics, among others. Interestingly, auxetic structures could also be tailored to provide passive adaptation to changes in environmental stimuli -- an adaptation of this concept is explored in this paper in the context of designing a novel load-adaptive gripper system. Defining the design in terms of repeating parametric unit cells from which the finite structure can be synthesized presents an attractive computationally-efficient approach to designing auxetic structures. This approach also decouples the optimization cost and the size of the overall structure, and avoids the pitfalls of system-scale design e.g., via topology optimization. In this paper, a surrogate-based design optimization framework is presented to implement the concept of passively load-adaptive structures (of given outer shape) synthesized from auxetic unit cells. Open-source meshing, FEA and Bayesian Optimization tools are integrated to develop this computational framework, enhancing it adopt-ability and extensibility. Demonstration of the concept and the underlying framework is performed by designing a simplified robotic gripper, with the objective to maximize the ratio of towards-load (gripping) horizontal displacement to the load-affected vertical displacement. Optimal auxetic cell-based design generated thereof is found to be four times better in terms of exhibited contact reaction force when compared to a design obtained with topology optimization that is subjected to the same specified maximum loading.
Xia Chen, Jimmy Abualdenien, Manav Mahan Singh
et al.
"What-if" questions are intuitively generated and commonly asked during the design process. Engineers and architects need to inherently conduct design decisions, progressing from one phase to another. They either use empirical domain experience, simulations, or data-driven methods to acquire consequential feedback. We take an example from an interdisciplinary domain of energy-efficient building design to argue that the current methods for decision support have limitations or deficiencies in four aspects: parametric independency identification, gaps in integrating knowledge-based and data-driven approaches, less explicit model interpretation, and ambiguous decision support boundaries. In this study, we first clarify the nature of dynamic experience in individuals and constant principal knowledge in design. Subsequently, we introduce causal inference into the domain. A four-step process is proposed to discover and analyze parametric dependencies in a mathematically rigorous and computationally efficient manner by identifying the causal diagram with interventions. The causal diagram provides a nexus for integrating domain knowledge with data-driven methods, providing interpretability and testability against the domain experience within the design space. Extracting causal structures from the data is close to the nature design reasoning process. As an illustration, we applied the properties of the proposed estimators through simulations. The paper concludes with a feasibility study demonstrating the proposed framework's realization.
This paper presents the TransBoat, a novel omnidirectional unmanned surface vehicle (USV) with a magnetbased docking system for overwater construction with wave disturbances. This is the first such USV that can build overwater structures by transporting modules. The TransBoat incorporates two features designed to reject wave disturbances. First, the TransBoat's expandable body structure can actively transform from a mono-hull into a multi-hull for stabilization in turbulent environments by extending its four outrigger hulls. Second, a real-time nonlinear model predictive control (NMPC) scheme is proposed for all shapes of the TransBoat to enhance its maneuverability and resist disturbance to its movement, based on a nonlinear dynamic model. An experimental approach is proposed to identify the parameters of the dynamic model, and a subsequent trajectory tracking test validates the dynamics, NMPC controller and system mobility. Further, docking experiments identify improved performance in the expanded form of the TransBoat compared with the contracted form, including an increased success rate (of ~ 10%) and reduced docking time (of ~ 40 s on average). Finally, a bridge construction test verifies our system design and the NMPC control method.
Ra'Fat Al-Msie'deen, Anas H. Blasi, Mohammed A. Alsuwaiket
Requirements engineering process intends to obtain software services and constraints. This process is essential to meet the customer's needs and expectations. This process includes three main activities in general. These are detecting requirements by interacting with software stakeholders, transferring these requirements into a standard document, and examining that the requirements really define the software that the client needs. Functional requirements are services that the software should deliver to the end-user. In addition, functional requirements describe how the software should respond to specific inputs, and how the software should behave in certain circumstances. This paper aims to develop a software requirements specification document of the electronic IT news magazine system. The electronic magazine provides users to post and view up-to-date IT news. Still, there is a lack in the literature of comprehensive studies about the construction of the electronic magazine software specification and design in conformance with the contemporary software development processes. Moreover, there is a need for a suitable research framework to support the requirements engineering process. The novelty of this paper is the construction of software specification and design of the electronic magazine by following the Al-Msie'deen research framework. All the documents of software requirements specification and design have been constructed to conform to the agile usage-centered design technique and the proposed research framework. A requirements specification and design are suggested and followed for the construction of the electronic magazine software. This study proved that involving users extensively in the process of software requirements specification and design will lead to the creation of dependable and acceptable software systems.
Petar Jokic, Erfan Azarkhish, Andrea Bonetti
et al.
Implementing embedded neural network processing at the edge requires efficient hardware acceleration that couples high computational performance with low power consumption. Driven by the rapid evolution of network architectures and their algorithmic features, accelerator designs are constantly updated and improved. To evaluate and compare hardware design choices, designers can refer to a myriad of accelerator implementations in the literature. Surveys provide an overview of these works but are often limited to system-level and benchmark-specific performance metrics, making it difficult to quantitatively compare the individual effect of each utilized optimization technique. This complicates the evaluation of optimizations for new accelerator designs, slowing-down the research progress. This work provides a survey of neural network accelerator optimization approaches that have been used in recent works and reports their individual effects on edge processing performance. It presents the list of optimizations and their quantitative effects as a construction kit, allowing to assess the design choices for each building block separately. Reported optimizations range from up to 10'000x memory savings to 33x energy reductions, providing chip designers an overview of design choices for implementing efficient low power neural network accelerators.
Targeting sustainability in our industrial society requires integrating specific criteria in the design process of products and processes. A paradigm shift is necessary in the economical, social and political systems to ensure the natural ecosystems preservation on the planet while fulfilling society needs. Various research methods have therefore emerged to change the way products and services are designed, developed, used and discarded considering territorial contexts. Design for Sustainability, Design for Sustainable Transition, Socially Responsible Design, post-growth design, etc. provide several methods integrating the sustainable principles in the design process. However those approaches remain mainly experimental and are limited to the industrial context. In parallel to those approaches a wide variety of grassroots initiatives have emerged in territories. They propose alternative ways to design systems and they integrate new constraints in a practical manner. This research therefore aims to confront the diversity of Design to Environment (DtE) approaches with 'grassroots' initiatives in order to understand the possible evolution of the integration of sustainability into the design process of products and services used in industry. This paper presents the first literature review results of a started project in 2020. An original research protocol is proposed in this paper, based on specific focus groups with grassroots initiatives practitioners and eco-design experts from research and industry. The presentation of four DtE frameworks are analysed in this paper. This research finally discusses the opportunity of integrating the grassroots enriched DtE frameworks by non-officialdesigners in life cycle engineering. This bottom-up process may drive an expression of sustainability in industry aligned with some emerging local socio-technical systems.
We construct a normal form for the walled Brauer algebra, together with the reduction algorithm. We apply normal form to calculate the numbers of monomials in generators with minimal length. We further utilize normal form to give explicit expressions for a generating set and annihilator ideal of a particular cyclic vector in a cell module.
BRICKxAR is a novel Augmented Reality (AR) instruction method for construction toys such as LEGO. With BRICKxAR, physical LEGO construction is guided by virtual bricks. Compared with the state-of-the-art, accuracy of the virtual - physical model alignment is significantly improved through a new design of marker-based registration, which can achieve an average error less than 1mm throughout the model. Realistic object occlusion is accomplished to reveal the true spatial relationship between physical and virtual bricks. LEGO players' hand detection and occlusion are realized to visualize the correct spatial relationship between real hands and virtual bricks, and allow virtual bricks to be "grasped" by real hands. The integration of these features makes AR instructions possible for small-parts assembly, validated through a working AR prototype for constructing LEGO Arc de Triomphe, quantitative measures of the accuracies of registration and occlusions, and heuristic evaluation of AR instruction features.
Conductive ferroelectric domain walls--ultra-narrow and configurable conduction paths, have been considered as essential building blocks for future programmable domain wall electronics. For applications in high density devices, it is imperative to explore the conductive domain walls in small confined systems while earlier investigations have hitherto focused on thin films or bulk single crystals, noting that the size-confined effects will certainly modulate seriously the domain structure and wall transport. Here, we demonstrate an observation and manipulation of conductive domain walls confined within small BiFeO3 nano-islands aligned in high density arrays. Using conductive atomic force microscopy (CAFM), we are able to distinctly visualize various types of conductive domain walls, including the head-to-head charged walls (CDWs), zigzag walls (zigzag-DWs), and typical 71° head-to-tail neutral walls (NDWs). The CDWs exhibit remarkably enhanced metallic conductivity with current of ~ nA order in magnitude and 104 times larger than that inside domains (0.01 ~ 0.1 pA), while the semiconducting NDWs allow also much smaller current ~ 10 pA than the CDWs. The substantially difference in conductivity for dissimilar walls enables additional manipulations of various wall conduction states for individual addressable nano-islands via electrically tuning of their domain structures. A controllable writing of four distinctive states by applying various scanning bias voltages is achieved, offering opportunities for developing multilevel high density memories.
Nick D. Austin, Nikolaos V. Sahinidis, Daniel W. Trahan
This article provides an introduction to and review of the field of computer-aided molecular design (CAMD). It is intended to be approachable for the absolute beginner as well as useful to the seasoned CAMD practitioner. We begin by discussing various quantitative structure-property relationships (QSPRs) which have been demonstrated to work well with CAMD problems. The methods discussed in this article are (1) group contribution methods, (2) topological indices, and (3) signature descriptors. Next, we present general optimization formulations for various forms of the CAMD problem. Common design constraints are discussed and structural feasibility constraints are provided for the three types of QSPRs addressed. We then detail useful techniques for approaching CAMD optimization problems, including decomposition methods, heuristic approaches, and mathematical programming strategies. Finally, we discuss many applications that have been addressed using CAMD.
MicroBooNE Collaboration, R. Acciarri, C. Adams
et al.
This paper describes the design and construction of the MicroBooNE liquid argon time projection chamber and associated systems. MicroBooNE is the first phase of the Short Baseline Neutrino program, located at Fermilab, and will utilize the capabilities of liquid argon detectors to examine a rich assortment of physics topics. In this document details of design specifications, assembly procedures, and acceptance tests are reported.
Evangelos Vrettos, Emre C. Kara, Jason MacDonald
et al.
This paper is the first part of a two-part series in which we present results from an experimental demonstration of frequency regulation in a commercial building test facility. In Part I, we introduce the test facility and develop relevant building models. Furthermore, we design a hierarchical controller that consists of three levels: a reserve scheduler, a building climate controller, and a fan speed controller for frequency regulation. We formulate the reserve scheduler as a robust optimization problem and introduce several approximations to reduce its complexity. The building climate controller is comprised of a robust model predictive controller and a Kalman filter. The frequency regulation controller consists of a feedback and a feedforward loop, provides fast responses, and is stable. Part I presents building model identification and controller tuning results, whereas Part II reports results from the operation of the hierarchical controller under frequency regulation.
The paper deals with recursive constructions for simple 3-designs based on other 3-designs having $(1, σ)$-resolution. The concept of $(1, σ)$-resolution may be viewed as a generalization of the parallelism for designs. We show the constructions and their applications to produce many previously unknown infinite families of simple 3-designs. We also include a discussion of $(1,σ)$-resolvability of the constructed designs.