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The session starts with an application of ML in test, then the design of a robust AI system is considered. Finally the focus is on fault injection in FPGAs. To run a trained DL model on an MCU, developers must have the necessary skills to handcraft network topologies and associated hyperparameters to fit a wide range of hardware requirements including operating frequency, embedded SRAM and embedded Flash memory along with the corresponding power consumption requirements.
Unfortunately, a hand-crafted design methodology poses multiple challenges: 1 AI and embedded developers exhibit different orthogonal skills, which do not meet each other during the development of AI applications until their validation in an operational environment 2 Tools for automated network design often assume virtually unlimited resources typically deep networks are trained on cloud- or GPU-based systems 3 The time-to-market from conception to realization of an AI system is usually quite long.
Consequently, mass market adoption of AI technologies at the deep edge is jeopardized. This talk will present our approach, along with its pros and cons with respect to multi-objective optimization usually adopted to reduce resource usage on cloud. A set of relevant results will be presented and discussed, providing an overview of the next open challenges and perspectives in the AutoTinyML field.
Autonomy is in the air: on one hand, automation is clearly a lever to improve safety margins; on another hand technologies are maturing, pulled by the automotive market. In this context, Airbus is building a concept airplane from a blank sheet with the objective to improve human-machine teaming for better overall performance. Autonomy technologies are the main enabler of this concept.
Benefit are expected both in a two-crew cockpit and eventually in Single Pilot Operations. Autonomy is a top technical focus area for Airbus. This session discusses technology innovation, experiences and processes in building autonomous systems.
The second paper presents an abstracted runtime for managing adaptation and integrating FPGA-accelerators to autonomous software framework , a show-case study integration into ROS is demonstrated. The third paper discusses current processes in engineering dependable collaborative autonomous systems and new buisness models based on agile approaches for innovative management. FPGAs are now part of the cloud acceleration-as-a-service portfolio offered by major cloud providers.
Cloud is naturally a multi-tenant platform. However, FPGA multitenancy raises security concerns, fueled by the recent research works that showed how a malicious cloud user can deploy remotely-controlled attacks to extract secret information from the FPGA co-tenants or inject faults. This hot-topic session aims at spreading the awareness of the threats and attack techniques and discussing the limitations of existing countermeasures, hopefully leading to a deeper understanding of the problem of developing the most appropriate mitigation techniques.
With the rise of neuromorphic computing, the traditional Von-Neumann architecture is finding it difficult to cope up with the rising demands of machine learning workloads. This requirement has fueled the search of technologies that can mimic human brain to efficiently combine both memory and computation with a single device. In this special session, we present the state-of-the-art research in the domain of in-memory computing. In particular, we take a look at memristors and its widespread application in neuromorphic computation.
We address the various circuit opportunities and challenges related to reliability and fault tolerance associated with them. Finally, we look at an emerging nanotechnologies involving co-integration of CMOS and FeFET, which has the potential to leverage memory and computation from a single device. Various NoC approaches have been proposed in the last two decades to provide efficient communication infrastructures in complex systems-on-chip.
Starting from 2D wired topologies the networks are expending to a large spectrum of architectures integrating emerging technologies.
This session presents advanced NoC-based designs and mechanisms leveraging different innovative solutions: optical, wireless or 3D vertical communication links. Those solutions are targeting applications where the complexity of the communication patterns are increasing and can become more critical than computation regarding the performance of the overall system. The session highlights innovative techniques for embedded power management optimization and prediction, ranging from formal verification, machine learning, and implementation in real systems.
The first paper presents the use of novel formal modeling techniques to bridge between run time decisions and design time exploration, the second paper introduces battery lifetime management using prediction model to optimize the operation points in real systems, the last paper describes a power prediction model based on Long Short-Term memory Neural Network.
This session addresses attacks and mitigation techniques at the application level. In the first paper, a novel approach is proposed for creating a timing-based covert channel on a dynamically partitioned shared Last Level Cache. Then several protection techniques are presented: an hardware mechanism to detect and mitigate cross-core cache attacks , a Row-Hammer attack mitigation technique based on time-varying activation probabilities, a non-intrusive malware detection method for PLCs, and a method for detecting memory corruption vulnerability exploits that relies on fuzzing and dynamic data flow analysis.
This session continues with a scheme for yield estimation of an SRAM array. Then, a method to mitigate the effect of stuck-at faults in ReRAM crossbar architectures is highlighted.
Panel session on different career opportunities having education in microelectronics. Panelists: Dr. David Moloney — Ubotica Technologies Dr. John Davis — Barcelona Supercomputing Center. With the slow-down of the pace of Moore's law, concerns have been expressed on IC design becoming completely commodified.
These concerns are misplaced: on the contrary, innovation through design is regaining importance with respect to innovation through technology evolution, as we need to become more creative to work around the hard brick walls in scaling devices. I will dray a couple of examples from my recent experience in designing machine learning accelerators and open source processors, to concretely illustrate how it is not only possible to find fun jobs in IC design, but also that there are interesting new options and business models to innovate in this area.
Modern autonomous systems - such as autonomous vehicles or robots - consist of two major components: a the decision making unit, which is often made up of one or more feedback control loops, and b a perception unit that feeds the environmental state to the control unit and is made up of camera, radar and lidar sensors and their associated processing algorithms and infrastructure. While there has been a lot of work on the formal verification of the decision making or the control unit, the ultimate correctness of the autonomous system also heavily relies on the behavior of the perception unit.
The verification of the correctness of the perception unit is however significantly more challenging and not much progress has been made here. This is a part of the overall challenge of verifying the correctness of autonomous systems. Virtual Conference and Exhibition, February User account menu Log in. Breadcrumb Home » Programme. Gillani, University of Twente, NL Abstract While the efficiency gains due to process technology improvements are reaching the fundamental limits of computing, emerging paradigms like approximate computing provide promising efficiency gains for error resilient applications.
However, the state-of-the-art approximate computing methodologies do not sufficiently address the accelerator designs for iterative and accumulation based algorithms. Keeping in view a wide range of such algorithms in digital signal processing, this thesis investigates systematic approximation methodologies to design high-efficiency accelerator architectures for iterative and accumulation based algorithms.
As a case study of such algorithms, we have applied our proposed approximate computing methodologies to a radio astronomy calibration application. More information However, this success of deep learning is based on a tremendous amount of energy consumption, which becomes one of the major obstacles to deploying the deep learning model on mobile devices.
To address this issue, many researchers have studied various methods for improving the energy efficiency of the neural networks to expand the applicability of deep learning. This dissertation is in line with those studies and contains mainly three approaches, including quantization, energy-efficient accelerator, and neuromorphic approach. Efficient FPGA implementation are proposed in the work along with relevant security analysis using prevalent metrics.
The dissertation aims to contribute to the formal verification of AMS circuits by generating accurate behavioral models that can be used for verification. As accurate behavioral models are often handwritten, this dissertation proposes an automatic abstraction method based on sampling a Spice netlist at transistor level with full Spice BSIM accuracy.
The approach generates a hybrid automaton HA that exhibits a linear behavior described by a state space representation in each of its locations, thereby modeling the nonlinear behavior of the netlist via multiple locations. Hence, due to the linearity of the obtained model, the approach is easily scalable. Various extensions exist for the models enhancing their exhibited behavior. This work answers to this need, presenting a collection of tools for efficient deployment of ConvNets on the edge.
The HLS process is usually controlled by user-given directives e. By using HLS, designers are able to rapidly generate different hardware implementations of the same application, without the burden of directly specifying the low level implementation in detail.
Nonetheless, the correlation among directives and resulting performance is often difficult to foresee and to quantify, and the high number of available directives leads to an exponential explosion in the number of possible configurations. In addition, sampling the design space involves a time-consuming hardware synthesis, making a brute-force exploration infeasible beyond very simple cases.
However, for a given application, only few directive settings result in Pareto-optimal solutions with respect to metrics such as area, run-time and power , while most are dominated.
The design space exploration problem aims at identifying close to Pareto-optimal implementations while synthesising only a small portion of the possible configurations from the design space. In my Ph. Moreover, I present new exploration methodologies able to automatically generate optimised implementations of hardware accelerators. The proposed approaches are able to retrieve a close approximation of the real Pareto solutions while synthesising only a small fraction of the possible design, either by smartly navigating their design space or by leveraging prior knowledge.
I also present a database of design space explorations whose goal is to push the research boundaries by offering to researchers a tool for the standardisation of exploration evaluation, and a reliable source of knowledge for machine learning based approaches. Lastly, the stepping-stones of a new approach relying on deep learning strategies with graph neural networks is presented.
Despite its high-density, non-volatility, near-zero leakage power, and immunity to radiation-induced particle strikes as the major advantages, STT-MRAM-based cache memory suffers from high error rates mainly due to retention failure, read disturbance, and write failure. Despite its high-density, non-volatility, near-zero leakage power, and immunity to radiation as the major advantages, STT-MRAM suffers from high error rates.
These errors, which are mainly retention failure, read disturbance, and write failure, are the major reliability challenge in Record No 8 Jointer Plane 70 STT-MRAM caches. However, the overall vulnerability of STT-MRAM caches, which its estimation is a must to design cost-efficient reliable caches has not been offered in none of previous studies. Meanwhile, all of the existing reliability improvement schemes in STT-MRAM caches are limited to overcome a single or two error types and the majority of them have adverse effect on other error types.
In this dissertation, we first propose a system-level framework for reliability exploration and characterization of errors behavior in STT-MRAM caches.
To this end, we formulate the cache vulnerability considering the inter-correlation of the error types including retention failure, read disturbance, and write failure as well as the dependency of error rates to workloads behavior and Process Variations PVs. Then, we investigate the effect of temperature on STT-MRAM cache error rate and demonstrate that heat accumulation increases the error rate by We also illustrate that this heat accumulation is mainly due to locality of committed write operations in the cache.
ROSTAM consists of four components: 1 a simple yet effective replacement policy, called TA-LRW, to prevent the heat accumulation in the cache and reduce the rate of all the three error types, 2 a novel tag array structure, so-called 3RSeT to reduce the error rate by eliminating a significant portion of tag reads, 3 an effective scheme, so-called REAP-Cache, to prevent the accumulation of read disturbance in cache blocks and completely eliminate the adverse effect of concealed reads on the cache reliability, and 4 a new ECC configuration, so-called ROBIN, to uniformly distribute the transitions between the codewords and maximize the ECC correction capability.
The experimental results using gem5 full-system simulator and a comprehensive set of multi-programmed workloads from SPEC CPU benchmark suite on a quad-core processor show that: 1 the rate of read disturbance error is reduced by The significantly reliability enhancement is achieved in the cost of less than 2.
In the era of big-data, a key challenge is to achieve close integration of logic and memory sub-systems, to overcome the von-Neumann bottleneck associated with the long-distance data transmission between logic and memory. Moreover, brain-inspired deep neural networks which have transformed the field of machine learning in recent years, are not widely deployable in edge devices, mainly due to the aforementioned bottleneck.
Therefore, there exists a need to explore solutions with tight logic-memory integration, in order to enable efficient computation for current and future generation of systems. Motivated by this, in this thesis, we harness the benefits offered by emerging technologies and propose devices, circuits, and systems which exhibit an amalgamation of logic and memory functionality. We propose two variants of memory devices: a Reconfigurable Ferroelectric transistors and b Valley-Coupled-Spin Hall effect-based magnetic random access memory, which exhibit unique logic-memory unification.
Exploiting the intriguing features of the proposed devices, we carry out a cross-layer exploration from device-to-circuits-to-systems for energy-efficient computing. We investigate a wide spectrum of applications for the proposed devices including embedded memories, non-volatile logic, compute-in-memory fabrics and artificial intelligence systems.
These devices have been deployed to collect and process an unprecedented amount of data around us. Also, to make full use of resources, often the system is shared among different applications. This raises a lot of security and privacy concerns. Meanwhile, memory and processor caches are essential components of modern computers, but they have been mainly designed for their functionality and performance, not for security. There are potential positive uses of hardware components that can improve security, but also, there are security attacks that make use of the vulnerabilities in hardware.
This dissertation consequently studies both the positive and negative security aspects of Dynamic Random Access Memories DRAMs and caches on commercial devices. The proposed DRAM Physically Unclonable Functions PUFs can be deployed today for higher security, especially in low-end IoT devices and embedded systems currently utilized in health care, home automation, transportation, or energy grids, which lack other security mechanisms.
The discovered cache LRU covert-channel attacks and DRAM temperature spying attacks show new types of vulnerabilities in today's systems, motivating new designs to protect applications in a shared system and to prevent malicious use of the physical features of the hardware.
Approximate Computing is a design paradigm particularly suited for error-resilient applications, where small losses in accuracy do not represent a significant reduction in the quality of the result. In these scenarios, energy consumption and resources employment such as electric power, or circuit area can be significantly improved at the expense of a slight reduction in output accuracy.
While Approximate Computing can be applied at different levels, my research focuses on the design of approximate hardware. In particular, my work explores Approximate Logic Synthesis, where the hardware functionality is automatically tuned to obtain more efficient counterparts, while always controlling the entailed error.
Functional modifications include, among others, removal or substitution of gates and signals. A fundamental prerequisite for the application of these modifications is an accurate error model of the circuit under exam. My Ph. These can, in turn, guide Approximate Logic Synthesis algorithms to optimal solutions and avoid expensive, time-consuming simulations. A precise error model allows to fully explore the design space and, potentially, adjust the desired level of accuracy even at runtime.
I have also contributed to the state of the art in ALS techniques by devising a circuit Record No 8 Jointer Plane Map pruning algorithm that produces efficient approximate circuits for given error constraints.
The innovative aspect of my work is that it exploits circuit topology and graph partitioning to identify circuit portions that impact to a smaller extent on the final output. With this information, ALS algorithms can improve their efficiency by acting first on those less-influent portions. Indeed, this error characterisation proves to be very effective in guiding and modeling approximate synthesis. The communication across these multiple cores is facilitated by the switch-based Network-on-Chip NoC for efficient and bursty on-chip communication.
The power and performance of these interconnect is a significant factor as the communication network consumes a considerable share of the power budget. In particular, the buffers used at every port of the NoC router consume considerable dynamic as well as static power. Powering off several components to stay within the TDP leads to the concept of dark silicon. In order to reduce the standby power of the network in such events, one looks for avenues in non-volatile memory NVM technologies.
These advantages include high density, good scalability, and low leakage power consumption. However, the buffers made from these memory technologies suffer from costly write operation and low write endurance.
Thus, in my PhD research, I proposed wear-levelling and write reduction techniques to enhance the lifetime and reduce the effect of the costly write operation of NVM buffers in the dark silicon scenario. We evaluate our proposed approaches on a multi-core full system simulator Gem5, with Garnet2. Generally, the approximate computing techniques have been developed and implemented either at algorithmic level or logic level or circuit level and with no feasibility of on-the-fly or Runtime change of approximation.
Thus, different from the existing methods, this thesis presents novel energy-efficient integrated approach of implementing approximate computing techniques from circuit level to the algorithmic level that incorporate the change of approximation for a given circuit at Runtime without incurring any extra hardware requirement.
Thus, developing an integrated approach of implementing runtime based approximate computing technique from circuit level abstract to algorithmic level abstract for image compression application.
The controller bases on a recent emerging computing model inspired by an amoeba to solve the Satisfiability problems SAT , which can represent various IoT applications. By extending the original algorithm to help the solver escape local minima more quickly and utilizing the community structure of different IoT applications, we developed a high efficient IoT controller which well understands the characteristics of different application domains and outperformed state-of-the-arts.
This is fueled by the trend towards implementing an increasing number of product functionalities in software that ends up managing huge amounts of data and implementing complex artificial-intelligence functionalities such as Advanced Driver Assistance Systems.
Manycores are able to satisfy, in a cost-efficient manner, the computing needs of embedded real-time industry. In this line, building as much as possible on manycore solutions deployed in the high-performance mainstream market, contribute to further reduce costs and increase availability.
However, commercial off the shelf COTS manycores bring several challenges for their adoption in the critical embedded market. In particular, the network-on-chip NoC has been shown to be the main resource in which contention arises, and hence hampers deriving tight bounds to the timing of tasks. However, since HPC components are not designed following the development process used in the automotive domain, some safety requirements are not met by default on those platforms.
CCFs can be avoided by enforcing diverse redundancy e. This thesis presents software and hardware techniques to achieve a diverse redundant execution in multiple HPC components to enable their usage in the automotive domain. Candidate, FR Abstract In recent years, the broad adoption and accessibility of the Internet of Things IoT have created major concerns for the manufacturers and enterprises in the hardware security domain.
However, embedded software developers often lack the knowledge to consider the hardware-based threats and their effects on important assets. To overcome such challenges, it is essential for the security specialists to provide the embedded developers with practical necessary tools and evaluation methods against hardware-based attacks. In this thesis work, we develop an evaluation methodology and an easy to use hardware security assessment framework, against major physical attacks e.
It can assist the software developers to detect their system vulnerabilities and to protect important assets. This study mimics a real experimental evaluation process and highlights the potential risks of ignoring the physical attacks. Based on a given VHDL model, various fault tolerant implementations can be automatically created and evaluated regarding their overhead and reliability improvement.
Due to the state-of-the-art accuracy of the models generated through DL, i. Besides energy efficiency, for safety-critical applications, reliability against technology-induced faults e. This Ph. This paradigm aims to reduce the computing costs of exact calculations by lowering the accuracy of their results.
In the last decade, many approximate circuits, particularly approximate adders and multipliers, have been reported in the literature. For an ongoing number of such approximate circuits, selecting those that minimize the required resources for designing and generating an approximate accelerator from a high-level specification while satisfying a previously defined accuracy constraint is a joint design space exploration and high-level synthesis challenge.
This dissertation proposes automated methods for designing and implementing approximate accelerators built with approximate arithmetic circuits.
This complexity has been introduced to address the challenging intended application scenarios, for instance, in automotive systems, which typically require several heterogeneous functions to be jointly implemented on-chip at once.
On the one hand, the complexity scales with the transistor count and, on the other hand, further non-functional aspects have to be considered, which leads to new demanding tasks during the state-of-the-art IC design and test.
Thus, new measures are required to achieve the required level of testability, debug and reliability of the resulting circuit. This thesis proposes several novel approaches to, in the end, pave the way for the next generation of IC, which can be successfully and reliable integrated even in safety-critical applications.
VecTHOR proposes a newly designed compression architecture, which combines a codeword-based compression, a dynamically configurable dictionary and a run-length encoding scheme. Another contribution concerns the design and implementation of a retargeting framework to process existing test data off-chip once prior-to the transfer without the need for an expensive test regeneration. Different techniques have been implemented to provide choosable trade-offs between the resulting the TDV as well as the TAT and the required run-time of the retargeting process.
These techniques include a fast heuristic approach and a formal optimization SAT-based method by invoking multiple objective functions.
Besides this, one contribution concerns the development of a hybrid embedded compression architecture, which is specifically designed for Low-Pin Count Test LPCT in the field of safety-critical systems enforcing a zero-defect policy.
This hybrid compression has been realized in close industrial cooperation with Infineon Germany. This approach allows reducing the resulting test time by a factor of approx. A further contribution is about the development of a new methodology to significantly enhance the robustness of sequential circuits against transient faults while neither introducing a large hardware overhead nor measurably impacting the latency of the circuit.
Application-specific knowledge is conducted by applying SAT-based techniques as well as BMC to achieve this, which yields the synthesis of a highly efficient fault detection mechanism. The proposed techniques are presented in detail and evaluated extensively by considering industrial-representative candidates, which clearly demonstrated the proposed approaches' efficacy.
Not only does FCN process binary information inherently, but it also allows for absolute low-power in-memory computing with an energy dissipation that is magnitudes below that of CMOS. However, physical design for FCN technologies is still in its infancy. This includes exact and heuristic techniques for placement, routing, clocking, and timing, formal verification, and debugging.
All proposed algorithms have been made publicly available in a holistic framework called fiction. Towards this goal, this work studies and analyzes the security vulnerabilities at hardware- and software levels to identify the potentially vulnerable components, SoCs, or systems in CPS. Based on these analyses, this work improves the security of the CPS by deploying efficient and low-overhead solutions.
These solutions can either identify the potential attacks during run-time or provide an efficient defend against these attacks. One of these limitations corresponds to the well known von Neumann bottleneck. The principle is to keep operations on large vectors in the C-RB of the SCM, reducing data movement to the CPU, thus drastically saving energy consumption of the overall system. To evaluate the proposed architecture, we use an instruction accurate platform based on Intel Pin software.
We achieve energy reduction up to 7. Our main goal is to reinvest on the MANGO cluster by providing a duality in its use for both large-scale hardware prototyping and high-performance computation. The trend towards sustainable computing also requires domain-specific heterogeneous hardware architectures, which promises further gains in energy efficiency. At the same time, todays HPC applications have evolved from monolithic simulations in a single domain to complex workflows crossing multiple disciplines.
In this paper, we explore how these trends affect the system design decisions and what this means for future computing architectures. By following these laws, the industry achieved an amazing relative decoupling between the improvement of key performance indicators KPIs , such as the number of transistors, from physical resource usage such as silicon wafers.
Concurrently, digital ICTs came from almost zero greenhouse gas emission GHG in the middle of the twentieth century to direct annual carbon footprint of approximately MT CO2e today. In this paper, we analyze the recent evolution of energy and carbon footprints from three ICT activity sub-sectors: semiconductor manufacturing, wireless Internet access and datacenter usage.
By adopting a Kaya-like decomposition in technology affluence and efficiency factors, we Record No 8 Jointer Plane Mode find out that the KPI increase failed to reach an absolute decoupling with respect to total energy consumption because the technology affluence increases more than the efficiency.
The same conclusion holds for GHG emissions except for datacenters, where recent investment in renewable energy sources lead to an absolute GHG reduction over the last years, despite a moderate energy increase. The largest structures, like GPT-3, have impressive results but also trigger questions about the resources required for their learning phase, in the order of magnitude of hundreds of MWh.
It is therefore of paramount importance to improve the efficiency of AI solutions in all their lifetime. The aim of this short tutorial is to raise awareness on the energy consumption of AI and to show different tracks to improve this problem, from distributed and federated learning, to optimization of Neural Networks and their data representation e.
In order to achieve predictable performance, one has, thus, to resort to software-based methods for controlling interference and reduce shared resource contention. Examples include memory bandwidth regulation and cache coloring, which can be implemented on hypervisor or operating system level. The Bosch talk will give insights on currently developed VIPs including potential pitfalls and different possible software-based mechanisms that are investigated for increasing performance predictability.
The drawback of such techniques is that they often require detailed knowledge of the hardware platform and its underlying IP, involve workload porting, or impose performance overheads that reduce the overall efficiency of the system.
Hardware can and should do more to assist software in this task: by providing identification, monitoring and control mechanisms that help system software observe the behavior of competing workloads and apportion the shared resources among them, hardware-based resource contention avoidance mechanisms can improve on the efficiency and efficacy of purely software-based approaches. The DSU provides an L3 cache partitioning scheme under software control that can limit cache contention between competing workloads in a DynamIQ processor cluster.
MPAM is an example of an architectural approach to resource contention avoidance and provides workload identification and attribution of memory traffic throughout the system, enabling software-controlled apportioning of system resources like cache capacity and memory bandwidth as well as monitoring of the performance of individual workloads. Finally, we provide examples of how these two complementary Arm technologies can work in tandem with system software to reduce shared resource contention, and we present the principles we believe will increase the determinism and predictability of real-time workloads that execute on high-performance Arm-based platforms.
One major challenge is to control the effect of interference on shared resources in an end-to-end fashion. In this talk, we discuss an alternative solution for controlling accesses to shared resources using admission control mechanisms.
The goal is to decouple the data layer, where transmission is performed, from the control layer responsible for allocation and arbitration of available resources.
The proposed approach allows to simplify system performance analysis by reducing the complexity of coupling different resources timing analysis, which usually leads to pessimistic formal guarantees or decreased performance and utilization. System performance analysis is an important ingredient for successful system design.
The high dynamic behaviour due to caches, memory controllers, etc. The talk of University of Pisa will discuss how the network calculus formalism can be used to predict performance in the context of vehicle integration platforms. Furthermore, it will show that the algorithm to compute those bounds is simple it can run in milliseconds time and can be adaptable to several DRAM models all it takes is to incorporate the DRAM timing parameters and constraint.
Benchmark results will show that the distance between lower and upper bounds obtained by the approach is immaterial for practical purposes few percentage points at most.
Usually, naive usage of HLS leads to accelerators with insufficient performance, so very time-consuming manual optimizations of input programs are necessary in such cases. Scalar replacement is a promising automatic memory access optimization that removes redundant memory accesses. However, it cannot handle loops with multiple write accesses to the same array, which poses a severe limitation of its applicability.
In this paper, we propose a new memory access optimization technique that breaks the limitation. Experimental results show that the proposed method achieves 2. Logic obfuscation thwarts IP theft by locking the functions of gate-level netlists using a locking key.
The complexity of circuit designs and migration to high level synthesis HLS expands the scope of logic locking to a higher abstraction. This is tedious and requires implementing them in the source code of the HLS tools. The use of dedicated hardware is an appealing solution to improve performance or efficiency. We propose a methodology to generate throughput oriented hardware accelerators for large integers multiplication leveraging High-Level Synthesis. The leaves often turn bright gold to yellow before they fall during autumn.
The flowers are mostly dioecious rarely monoecious and appear in early spring before the leaves. They are borne in long, drooping, sessile or pedunculate catkins produced from buds formed in the axils of the leaves of the previous year. The flowers are each seated in a cup-shaped disk which is borne on the base of a scale which is itself attached to the rachis of the catkin. The scales are obovate, lobed, and fringed, membranous, hairy or smooth, and usually caducous.
The male flowers are without calyx or corolla , and comprise a group of four to 60 stamens inserted on a disk; filaments are short and pale yellow; anthers are oblong, purple or red, introrse, and two-celled; the cells open longitudinally.
The female flower also has no calyx or corolla, and comprises a single-celled ovary seated in a cup-shaped disk. The style is short, with two to four stigmata, variously lobed, and numerous ovules. Pollination is by wind, with the female catkins lengthening considerably between pollination and maturity.
The fruit is a two- to four-valved dehiscent capsule , green to reddish-brown, mature in midsummer, containing numerous minute light brown seeds surrounded by tufts of long, soft, white hairs which aid wind dispersal. Poplars of the cottonwood section are often wetlands or riparian trees. The aspens are among the most important boreal broadleaf trees. Poplars and aspens are important food plants for the larvae of a large number of Lepidoptera species.
Pleurotus populinus , the aspen oyster mushroom, is found exclusively on dead wood of Populus trees in North America. Several species of Populus in the United Kingdom and other parts of Europe have experienced heavy dieback ; this is thought in part to be due to Sesia apiformis which bores into the trunk of the tree during its larval stage.
The genus Populus has traditionally been divided into six sections on the basis of leaf and flower characters; [3] [6] this classification is followed below. Recent genetic studies have largely supported this, confirming some previously suspected reticulate evolution due to past hybridisation and introgression events between the groups.
Some species noted below had differing relationships indicated by their nuclear DNA paternally inherited and chloroplast DNA sequences maternally inherited , a clear indication of likely hybrid origin.
The oldest easily identifiable fossil of this genus belongs to Poplus wilmattae , and comes from the Late Paleocene about 58 Ma. Many poplars are grown as ornamental trees , with numerous cultivars used. They have the advantage of growing to a very large size at a rapid pace. Almost all poplars take root readily from cuttings or where broken branches lie on the ground they also often have remarkable suckering abilities, and can form huge colonies from a single original tree, such as the famous Pando forest made of thousands of Populus tremuloides clones.
Trees with fastigiate erect, columnar branching are particularly popular, and are widely grown across Europe and southwest Asia.
However, like willows , poplars have very vigorous and invasive root systems stretching up to 40 metres ft from the trees; planting close to houses or ceramic water pipes may result in damaged foundations and cracked walls and pipes due to their search for moisture.
A simple, reproducible, high-frequency micropropagation protocol in eastern cottonwood Populus deltoides has been reported by Yadav et al. In India, the poplar is grown commercially by farmers, mainly in the Punjab region. Common poplar varieties are:. The trees are grown from kalam or cuttings, harvested annually in January and February, and commercially available up to 15 November.
Most commonly used to make plywood , Yamuna Nagar in Haryana state has a large plywood industry reliant upon poplar. It is graded according to sizes known as "over" over 24 inches mm , "under" 18—24 inches — mm , and "sokta" less than 18 inches mm.
Although the wood from Populus is known as poplar wood, a common high-quality hardwood "poplar" with a greenish colour is actually from an unrelated genus Liriodendron. Populus wood is a lighter, more porous material. Its flexibility and close grain make it suitable for a number of applications, similar to those for willow. The Greeks and Etruscans made shields of poplar, and Pliny the Elder also recommended poplar for this purpose. Interest exists in using poplar as an energy crop for biomass , in energy forestry systems, particularly in light of its high energy-in to energy-out ratio, large carbon mitigation potential, and fast growth.
In the United Kingdom , poplar as with fellow energy crop willow is typically grown in a short rotation coppice system for two to five years with single or multiple stems , then harvested and burned - the yield of some varieties can be as high as 12 oven-dry tonnes per hectare every year.
Biofuel is another option for using poplar as bioenergy supply. Should I ever be compensated to write, I will make full disclosure. I always give honest opinions, findings, and experiences on products. The views and opinions expressed on this blog are purely our own. Any product claim, statistic, quote or other representation about a product or service should be verified with the manufacturer, provider or party in question. All content on The Wood Whisperer is copyrighted, and may not be reprinted in full form without my written consent.
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